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Most Popular Content – September 2026
Please find below a list of the most popular posts on this website for September 2026. Which one is your favourite?
1. Why Data Normalisation Has Gone Out of Fashion 2. Podcast: Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 3. Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 4. The Top 20 Docker Containers Every Private Investor Should Run 5. Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier SRi 6. Court Guinness Big Birthday Update 2026 7. Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 8. Early September 2026 Advertised Requirements 9. A Comparative Analysis of the Jeanneau Merry Fisher and Beneteau Antares: Choosing Your Perfect Cruising Companion 10. Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 11. Podcast: Building a Bloomberg Terminal on a Budget Using Open Source Software 12. Microblog: Visitor Statistics : September 2026 13. Retro Road Test: Peugeot 306 1.4 XL v Vauxhall Astra 1.4 GLS 14. Mid September 2026 Advertised Requirements 15. Podcast: A few things you do not know about Court. 16. The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 17. Court Report 05/09/2026 (1) @ 19:35 18. Podcast: So The Public Sector Are Complaining Again! 19. Podcast:The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 20. Nextcloud Vs Pydio 21. Podcast: Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 22. Speech Given By Court Guinness On Reaching 1,000 Days With a Continual Headache 23. Podcast: Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier Sri 24. Fiat Cinquecento vs Peugeot 106 25. Retro Road Test: Jaguar X-Type 2.5 V6 vs Rover 75 2.5 V6 26. Joint Venture Opportunity 27. The Rap Sheet 28. A few things you do not know about Court. 29. 51 30. Coastal Lifestyle Business Sought – September 2026 31. A Retro Road Test! Happy Eater Vs Little Chef 32. Building a Bloomberg Terminal on a Budget Using Open Source Software 33. Property Requirement 09/2026 – North West UK 34. New Jobs Available – Business Consultants 35. Court Report 10/09/2026 (1) @ 19:00 36. Podcast: Fiat Cinquecento vs Peugeot 106 37. Event: Headache Party 07/10/2026 38. 1,000 Days Party 39. 2,000 Days Party 40. Court Report 14/09/2026 (1) @ 21:55 41. Mid September 2026 Advertised Requirements 42. Podcast: The Top 20 Docker Containers Every Private Investor Should Run 43. Contact Court 44. Microblog: Headache Blog 18/09/2026 45. Lucy’s Rap Sheet – September 2026 46. Late September 2026 Advertised Requirements 47. Court Report 30/08/2026 (1) @ 19:35 48. Retro Tech: A Tale of Two Titans: Commodore 64 vs. Amstrad CPC 464 49. Lucy’s Message September 2026 50. So The Public Sector Are Complaining Again! 51. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 52. Retro Road Test: Ford Granada vs Vauxhall Carlton — 1990 road test (2.0-litre) 53. Podcast: Early August 2026 Advertised Requirements 54. M62 Belt Requirements September 2026 55. Doing A Crap Job 56. Podcast: Early September 2026 Advertised Requirements 57. A Comprehensive Comparison Between Lotus Approach and Microsoft Access 58. Retro Road Test: 1983 Vauxhall Astra GTE vs. 1983 Volkswagen Golf GTI 59. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 60. House Swap Brochure 61. Retro Road Test: BMW 735i vs Lexus LS400 (1990) 62. Podcast: Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 63. Retro Road Tests: Vehicle List: September 2026 64. Welsh Borders Requirements September 2026 65. Retro Road Tests: Vehicle List: September 2025 66. Update On This Site 10/04/2021 67. Revised Full Road Test Directory – September 2026 68. Homepage (Latest posts) 69. MicroBlog: August 2026 Statistics 70. Ewell Systems Appointed By Adobe 71. Most Popular Content – August 2026 72. Restaurant Premises Required In Tyne and Wear 73. Podcast Update: September 2024 74. Microblog: Headache Blog 04/01/2023 75. It’s Lucy I Have Moved My Website Here 76. Court Report 14/08/2026 (1) @ 05:10 77. Vending Business Opportunities : December 2020 78. Shopping: Samsung Telly’s March 2017 79. Bullet Point Update 29/05/20 80. Sheffield/Peak District/South Yorkshire Requirements April 2025 81. Why the NHS 1–10 Pain Scale is Not Fit for Purpose and Should Be Replaced 82. Retro Road Test: Mazda Xedos 6 vs Volvo 850 (1993 specs, UK) 83. A Quick Word About Content In The Trade House Magazine – August 2020 84. Retro Road Test: Talbot Tagora vs Ford Granada 85. Microblog: 26/03/2020 Irish IP Address blocking 86. Please consider subscribing to my newsletter 87. National Balance Awareness Week 2026 88. BlueSky – September 2026 89. Stolen Watch – September 2026 90. Retro Road Test: Alfa Romeo 164 Cloverleaf vs Lancia Thema Turbo (1991) 91. Retro Road Test: Fiat 131 vs Ford Cortina (1981 UK Specs) 92. Mid May Tech Update 1/Latest Microsoft Surface Review. 93. Microblog: Target Reached: 2,000,000 94. Repost: Market Comment March 2020 95. Podcast: Branding I devised 96. Court Report 19/09/2026 (1) @ 17:30 97. Retro Road Test: Audi A6 Allroad vs Volvo XC70 (2002) 98. Property Requirement 09/2026 – South Coast UK 99. Retro Road Test: Alfa Romeo 75 vs. Saab 900: 100. Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 101. Podcast: Retro Road Test: Lancia Thema vs Vauxhall Carlton (1990, UK Spec) 102. Why “Randy Mandy” Should Be a Warning to All Employers and Boards 103. Fuel Shortages 104. Liveaboard Boat Required June 2026 105. Retro Road Test: Suzuki Hayabusa vs Honda Fireblade (1999) 106. Podcast: Teacher’s Arrogance: Why Is It A Big Problem? 107. Announcing The Quick Stats Widget 108. Media Conference: NHS Business Services Authority. Mr Court Guinness and Fraud. 109. Boat Test: Fleming 58 and the Grand Banks Aleutian 59 110. Podcast: Testing Latest Court Report Format As A Podcast 111. Land and Sites Required Nationwide 112. Late August 2026 Advertised Requirements 113. Podcast Update: September 2026 114. Samsung Telly’s 115. Unpaid, Late, and Aged Debt from 01/07/2022 116. Mini Update From Court Guinness 117. Updated Site List June 2021 118. Retro Tech: Borland Sidekick Vs Symantec Act! 119. Podcast: Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 120. Retro Road test: Toyota Land Cruiser vs Mitsubishi Shogun 121. Podcast: I Wonder What Is Happening In Retail 122. The Latest News From Court 123. Olympics Planning 124. Press Release 125. The Surrey Special Edition Slideshare #2024 #blog #bulletin #community #courtGuinness #news #report #september2024 #topPosts #writing -
Most Popular Content – September 2026
Please find below a list of the most popular posts on this website for September 2026. Which one is your favourite?
1. Why Data Normalisation Has Gone Out of Fashion 2. Podcast: Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 3. Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 4. The Top 20 Docker Containers Every Private Investor Should Run 5. Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier SRi 6. Court Guinness Big Birthday Update 2026 7. Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 8. Early September 2026 Advertised Requirements 9. A Comparative Analysis of the Jeanneau Merry Fisher and Beneteau Antares: Choosing Your Perfect Cruising Companion 10. Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 11. Podcast: Building a Bloomberg Terminal on a Budget Using Open Source Software 12. Microblog: Visitor Statistics : September 2026 13. Retro Road Test: Peugeot 306 1.4 XL v Vauxhall Astra 1.4 GLS 14. Mid September 2026 Advertised Requirements 15. Podcast: A few things you do not know about Court. 16. The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 17. Court Report 05/09/2026 (1) @ 19:35 18. Podcast: So The Public Sector Are Complaining Again! 19. Podcast:The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 20. Nextcloud Vs Pydio 21. Podcast: Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 22. Speech Given By Court Guinness On Reaching 1,000 Days With a Continual Headache 23. Podcast: Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier Sri 24. Fiat Cinquecento vs Peugeot 106 25. Retro Road Test: Jaguar X-Type 2.5 V6 vs Rover 75 2.5 V6 26. Joint Venture Opportunity 27. The Rap Sheet 28. A few things you do not know about Court. 29. 51 30. Coastal Lifestyle Business Sought – September 2026 31. A Retro Road Test! Happy Eater Vs Little Chef 32. Building a Bloomberg Terminal on a Budget Using Open Source Software 33. Property Requirement 09/2026 – North West UK 34. New Jobs Available – Business Consultants 35. Court Report 10/09/2026 (1) @ 19:00 36. Podcast: Fiat Cinquecento vs Peugeot 106 37. Event: Headache Party 07/10/2026 38. 1,000 Days Party 39. 2,000 Days Party 40. Court Report 14/09/2026 (1) @ 21:55 41. Mid September 2026 Advertised Requirements 42. Podcast: The Top 20 Docker Containers Every Private Investor Should Run 43. Contact Court 44. Microblog: Headache Blog 18/09/2026 45. Lucy’s Rap Sheet – September 2026 46. Late September 2026 Advertised Requirements 47. Court Report 30/08/2026 (1) @ 19:35 48. Retro Tech: A Tale of Two Titans: Commodore 64 vs. Amstrad CPC 464 49. Lucy’s Message September 2026 50. So The Public Sector Are Complaining Again! 51. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 52. Retro Road Test: Ford Granada vs Vauxhall Carlton — 1990 road test (2.0-litre) 53. Podcast: Early August 2026 Advertised Requirements 54. M62 Belt Requirements September 2026 55. Doing A Crap Job 56. Podcast: Early September 2026 Advertised Requirements 57. A Comprehensive Comparison Between Lotus Approach and Microsoft Access 58. Retro Road Test: 1983 Vauxhall Astra GTE vs. 1983 Volkswagen Golf GTI 59. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 60. House Swap Brochure 61. Retro Road Test: BMW 735i vs Lexus LS400 (1990) 62. Podcast: Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 63. Retro Road Tests: Vehicle List: September 2026 64. Welsh Borders Requirements September 2026 65. Retro Road Tests: Vehicle List: September 2025 66. Update On This Site 10/04/2021 67. Revised Full Road Test Directory – September 2026 68. Homepage (Latest posts) 69. MicroBlog: August 2026 Statistics 70. Ewell Systems Appointed By Adobe 71. Most Popular Content – August 2026 72. Restaurant Premises Required In Tyne and Wear 73. Podcast Update: September 2024 74. Microblog: Headache Blog 04/01/2023 75. It’s Lucy I Have Moved My Website Here 76. Court Report 14/08/2026 (1) @ 05:10 77. Vending Business Opportunities : December 2020 78. Shopping: Samsung Telly’s March 2017 79. Bullet Point Update 29/05/20 80. Sheffield/Peak District/South Yorkshire Requirements April 2025 81. Why the NHS 1–10 Pain Scale is Not Fit for Purpose and Should Be Replaced 82. Retro Road Test: Mazda Xedos 6 vs Volvo 850 (1993 specs, UK) 83. A Quick Word About Content In The Trade House Magazine – August 2020 84. Retro Road Test: Talbot Tagora vs Ford Granada 85. Microblog: 26/03/2020 Irish IP Address blocking 86. Please consider subscribing to my newsletter 87. National Balance Awareness Week 2026 88. BlueSky – September 2026 89. Stolen Watch – September 2026 90. Retro Road Test: Alfa Romeo 164 Cloverleaf vs Lancia Thema Turbo (1991) 91. Retro Road Test: Fiat 131 vs Ford Cortina (1981 UK Specs) 92. Mid May Tech Update 1/Latest Microsoft Surface Review. 93. Microblog: Target Reached: 2,000,000 94. Repost: Market Comment March 2020 95. Podcast: Branding I devised 96. Court Report 19/09/2026 (1) @ 17:30 97. Retro Road Test: Audi A6 Allroad vs Volvo XC70 (2002) 98. Property Requirement 09/2026 – South Coast UK 99. Retro Road Test: Alfa Romeo 75 vs. Saab 900: 100. Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 101. Podcast: Retro Road Test: Lancia Thema vs Vauxhall Carlton (1990, UK Spec) 102. Why “Randy Mandy” Should Be a Warning to All Employers and Boards 103. Fuel Shortages 104. Liveaboard Boat Required June 2026 105. Retro Road Test: Suzuki Hayabusa vs Honda Fireblade (1999) 106. Podcast: Teacher’s Arrogance: Why Is It A Big Problem? 107. Announcing The Quick Stats Widget 108. Media Conference: NHS Business Services Authority. Mr Court Guinness and Fraud. 109. Boat Test: Fleming 58 and the Grand Banks Aleutian 59 110. Podcast: Testing Latest Court Report Format As A Podcast 111. Land and Sites Required Nationwide 112. Late August 2026 Advertised Requirements 113. Podcast Update: September 2026 114. Samsung Telly’s 115. Unpaid, Late, and Aged Debt from 01/07/2022 116. Mini Update From Court Guinness 117. Updated Site List June 2021 118. Retro Tech: Borland Sidekick Vs Symantec Act! 119. Podcast: Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 120. Retro Road test: Toyota Land Cruiser vs Mitsubishi Shogun 121. Podcast: I Wonder What Is Happening In Retail 122. The Latest News From Court 123. Olympics Planning 124. Press Release 125. The Surrey Special Edition Slideshare #2024 #blog #bulletin #community #courtGuinness #news #report #september2024 #topPosts #writing -
Most Popular Content – September 2026
Please find below a list of the most popular posts on this website for September 2026. Which one is your favourite?
1. Why Data Normalisation Has Gone Out of Fashion 2. Podcast: Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 3. Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 4. The Top 20 Docker Containers Every Private Investor Should Run 5. Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier SRi 6. Court Guinness Big Birthday Update 2026 7. Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 8. Early September 2026 Advertised Requirements 9. A Comparative Analysis of the Jeanneau Merry Fisher and Beneteau Antares: Choosing Your Perfect Cruising Companion 10. Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 11. Podcast: Building a Bloomberg Terminal on a Budget Using Open Source Software 12. Microblog: Visitor Statistics : September 2026 13. Retro Road Test: Peugeot 306 1.4 XL v Vauxhall Astra 1.4 GLS 14. Mid September 2026 Advertised Requirements 15. Podcast: A few things you do not know about Court. 16. The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 17. Court Report 05/09/2026 (1) @ 19:35 18. Podcast: So The Public Sector Are Complaining Again! 19. Podcast:The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 20. Nextcloud Vs Pydio 21. Podcast: Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 22. Speech Given By Court Guinness On Reaching 1,000 Days With a Continual Headache 23. Podcast: Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier Sri 24. Fiat Cinquecento vs Peugeot 106 25. Retro Road Test: Jaguar X-Type 2.5 V6 vs Rover 75 2.5 V6 26. Joint Venture Opportunity 27. The Rap Sheet 28. A few things you do not know about Court. 29. 51 30. Coastal Lifestyle Business Sought – September 2026 31. A Retro Road Test! Happy Eater Vs Little Chef 32. Building a Bloomberg Terminal on a Budget Using Open Source Software 33. Property Requirement 09/2026 – North West UK 34. New Jobs Available – Business Consultants 35. Court Report 10/09/2026 (1) @ 19:00 36. Podcast: Fiat Cinquecento vs Peugeot 106 37. Event: Headache Party 07/10/2026 38. 1,000 Days Party 39. 2,000 Days Party 40. Court Report 14/09/2026 (1) @ 21:55 41. Mid September 2026 Advertised Requirements 42. Podcast: The Top 20 Docker Containers Every Private Investor Should Run 43. Contact Court 44. Microblog: Headache Blog 18/09/2026 45. Lucy’s Rap Sheet – September 2026 46. Late September 2026 Advertised Requirements 47. Court Report 30/08/2026 (1) @ 19:35 48. Retro Tech: A Tale of Two Titans: Commodore 64 vs. Amstrad CPC 464 49. Lucy’s Message September 2026 50. So The Public Sector Are Complaining Again! 51. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 52. Retro Road Test: Ford Granada vs Vauxhall Carlton — 1990 road test (2.0-litre) 53. Podcast: Early August 2026 Advertised Requirements 54. M62 Belt Requirements September 2026 55. Doing A Crap Job 56. Podcast: Early September 2026 Advertised Requirements 57. A Comprehensive Comparison Between Lotus Approach and Microsoft Access 58. Retro Road Test: 1983 Vauxhall Astra GTE vs. 1983 Volkswagen Golf GTI 59. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 60. House Swap Brochure 61. Retro Road Test: BMW 735i vs Lexus LS400 (1990) 62. Podcast: Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 63. Retro Road Tests: Vehicle List: September 2026 64. Welsh Borders Requirements September 2026 65. Retro Road Tests: Vehicle List: September 2025 66. Update On This Site 10/04/2021 67. Revised Full Road Test Directory – September 2026 68. Homepage (Latest posts) 69. MicroBlog: August 2026 Statistics 70. Ewell Systems Appointed By Adobe 71. Most Popular Content – August 2026 72. Restaurant Premises Required In Tyne and Wear 73. Podcast Update: September 2024 74. Microblog: Headache Blog 04/01/2023 75. It’s Lucy I Have Moved My Website Here 76. Court Report 14/08/2026 (1) @ 05:10 77. Vending Business Opportunities : December 2020 78. Shopping: Samsung Telly’s March 2017 79. Bullet Point Update 29/05/20 80. Sheffield/Peak District/South Yorkshire Requirements April 2025 81. Why the NHS 1–10 Pain Scale is Not Fit for Purpose and Should Be Replaced 82. Retro Road Test: Mazda Xedos 6 vs Volvo 850 (1993 specs, UK) 83. A Quick Word About Content In The Trade House Magazine – August 2020 84. Retro Road Test: Talbot Tagora vs Ford Granada 85. Microblog: 26/03/2020 Irish IP Address blocking 86. Please consider subscribing to my newsletter 87. National Balance Awareness Week 2026 88. BlueSky – September 2026 89. Stolen Watch – September 2026 90. Retro Road Test: Alfa Romeo 164 Cloverleaf vs Lancia Thema Turbo (1991) 91. Retro Road Test: Fiat 131 vs Ford Cortina (1981 UK Specs) 92. Mid May Tech Update 1/Latest Microsoft Surface Review. 93. Microblog: Target Reached: 2,000,000 94. Repost: Market Comment March 2020 95. Podcast: Branding I devised 96. Court Report 19/09/2026 (1) @ 17:30 97. Retro Road Test: Audi A6 Allroad vs Volvo XC70 (2002) 98. Property Requirement 09/2026 – South Coast UK 99. Retro Road Test: Alfa Romeo 75 vs. Saab 900: 100. Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 101. Podcast: Retro Road Test: Lancia Thema vs Vauxhall Carlton (1990, UK Spec) 102. Why “Randy Mandy” Should Be a Warning to All Employers and Boards 103. Fuel Shortages 104. Liveaboard Boat Required June 2026 105. Retro Road Test: Suzuki Hayabusa vs Honda Fireblade (1999) 106. Podcast: Teacher’s Arrogance: Why Is It A Big Problem? 107. Announcing The Quick Stats Widget 108. Media Conference: NHS Business Services Authority. Mr Court Guinness and Fraud. 109. Boat Test: Fleming 58 and the Grand Banks Aleutian 59 110. Podcast: Testing Latest Court Report Format As A Podcast 111. Land and Sites Required Nationwide 112. Late August 2026 Advertised Requirements 113. Podcast Update: September 2026 114. Samsung Telly’s 115. Unpaid, Late, and Aged Debt from 01/07/2022 116. Mini Update From Court Guinness 117. Updated Site List June 2021 118. Retro Tech: Borland Sidekick Vs Symantec Act! 119. Podcast: Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 120. Retro Road test: Toyota Land Cruiser vs Mitsubishi Shogun 121. Podcast: I Wonder What Is Happening In Retail 122. The Latest News From Court 123. Olympics Planning 124. Press Release 125. The Surrey Special Edition Slideshare #2024 #blog #bulletin #community #courtGuinness #news #report #september2024 #topPosts #writing -
Most Popular Content – September 2026
Please find below a list of the most popular posts on this website for September 2026. Which one is your favourite?
1. Why Data Normalisation Has Gone Out of Fashion 2. Podcast: Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 3. Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 4. The Top 20 Docker Containers Every Private Investor Should Run 5. Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier SRi 6. Court Guinness Big Birthday Update 2026 7. Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 8. Early September 2026 Advertised Requirements 9. A Comparative Analysis of the Jeanneau Merry Fisher and Beneteau Antares: Choosing Your Perfect Cruising Companion 10. Retro Road Test: Citroën Xantia 1.8i SX v Ford Mondeo 1.8 LX 11. Podcast: Building a Bloomberg Terminal on a Budget Using Open Source Software 12. Microblog: Visitor Statistics : September 2026 13. Retro Road Test: Peugeot 306 1.4 XL v Vauxhall Astra 1.4 GLS 14. Mid September 2026 Advertised Requirements 15. Podcast: A few things you do not know about Court. 16. The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 17. Court Report 05/09/2026 (1) @ 19:35 18. Podcast: So The Public Sector Are Complaining Again! 19. Podcast:The Self-Hosted Family Office: Open Source Tools for High Net Worth Investors 20. Nextcloud Vs Pydio 21. Podcast: Retro Road Test: Alfa Romeo 155 2.0 Twin Spark vs BMW 320i 22. Speech Given By Court Guinness On Reaching 1,000 Days With a Continual Headache 23. Podcast: Retro Road Test: Nissan Bluebird ZX Turbo vs Vauxhall Cavalier Sri 24. Fiat Cinquecento vs Peugeot 106 25. Retro Road Test: Jaguar X-Type 2.5 V6 vs Rover 75 2.5 V6 26. Joint Venture Opportunity 27. The Rap Sheet 28. A few things you do not know about Court. 29. 51 30. Coastal Lifestyle Business Sought – September 2026 31. A Retro Road Test! Happy Eater Vs Little Chef 32. Building a Bloomberg Terminal on a Budget Using Open Source Software 33. Property Requirement 09/2026 – North West UK 34. New Jobs Available – Business Consultants 35. Court Report 10/09/2026 (1) @ 19:00 36. Podcast: Fiat Cinquecento vs Peugeot 106 37. Event: Headache Party 07/10/2026 38. 1,000 Days Party 39. 2,000 Days Party 40. Court Report 14/09/2026 (1) @ 21:55 41. Mid September 2026 Advertised Requirements 42. Podcast: The Top 20 Docker Containers Every Private Investor Should Run 43. Contact Court 44. Microblog: Headache Blog 18/09/2026 45. Lucy’s Rap Sheet – September 2026 46. Late September 2026 Advertised Requirements 47. Court Report 30/08/2026 (1) @ 19:35 48. Retro Tech: A Tale of Two Titans: Commodore 64 vs. Amstrad CPC 464 49. Lucy’s Message September 2026 50. So The Public Sector Are Complaining Again! 51. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 52. Retro Road Test: Ford Granada vs Vauxhall Carlton — 1990 road test (2.0-litre) 53. Podcast: Early August 2026 Advertised Requirements 54. M62 Belt Requirements September 2026 55. Doing A Crap Job 56. Podcast: Early September 2026 Advertised Requirements 57. A Comprehensive Comparison Between Lotus Approach and Microsoft Access 58. Retro Road Test: 1983 Vauxhall Astra GTE vs. 1983 Volkswagen Golf GTI 59. Retro Road Test: BMW 750iL vs Mercedes-Benz 500SEL (1992) 60. House Swap Brochure 61. Retro Road Test: BMW 735i vs Lexus LS400 (1990) 62. Podcast: Retro Road Test: 1986 Austin Maestro 1.3 vs Toyota Corolla 1.3 63. Retro Road Tests: Vehicle List: September 2026 64. Welsh Borders Requirements September 2026 65. Retro Road Tests: Vehicle List: September 2025 66. Update On This Site 10/04/2021 67. Revised Full Road Test Directory – September 2026 68. Homepage (Latest posts) 69. MicroBlog: August 2026 Statistics 70. Ewell Systems Appointed By Adobe 71. Most Popular Content – August 2026 72. Restaurant Premises Required In Tyne and Wear 73. Podcast Update: September 2024 74. Microblog: Headache Blog 04/01/2023 75. It’s Lucy I Have Moved My Website Here 76. Court Report 14/08/2026 (1) @ 05:10 77. Vending Business Opportunities : December 2020 78. Shopping: Samsung Telly’s March 2017 79. Bullet Point Update 29/05/20 80. Sheffield/Peak District/South Yorkshire Requirements April 2025 81. Why the NHS 1–10 Pain Scale is Not Fit for Purpose and Should Be Replaced 82. Retro Road Test: Mazda Xedos 6 vs Volvo 850 (1993 specs, UK) 83. A Quick Word About Content In The Trade House Magazine – August 2020 84. Retro Road Test: Talbot Tagora vs Ford Granada 85. Microblog: 26/03/2020 Irish IP Address blocking 86. Please consider subscribing to my newsletter 87. National Balance Awareness Week 2026 88. BlueSky – September 2026 89. Stolen Watch – September 2026 90. Retro Road Test: Alfa Romeo 164 Cloverleaf vs Lancia Thema Turbo (1991) 91. Retro Road Test: Fiat 131 vs Ford Cortina (1981 UK Specs) 92. Mid May Tech Update 1/Latest Microsoft Surface Review. 93. Microblog: Target Reached: 2,000,000 94. Repost: Market Comment March 2020 95. Podcast: Branding I devised 96. Court Report 19/09/2026 (1) @ 17:30 97. Retro Road Test: Audi A6 Allroad vs Volvo XC70 (2002) 98. Property Requirement 09/2026 – South Coast UK 99. Retro Road Test: Alfa Romeo 75 vs. Saab 900: 100. Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 101. Podcast: Retro Road Test: Lancia Thema vs Vauxhall Carlton (1990, UK Spec) 102. Why “Randy Mandy” Should Be a Warning to All Employers and Boards 103. Fuel Shortages 104. Liveaboard Boat Required June 2026 105. Retro Road Test: Suzuki Hayabusa vs Honda Fireblade (1999) 106. Podcast: Teacher’s Arrogance: Why Is It A Big Problem? 107. Announcing The Quick Stats Widget 108. Media Conference: NHS Business Services Authority. Mr Court Guinness and Fraud. 109. Boat Test: Fleming 58 and the Grand Banks Aleutian 59 110. Podcast: Testing Latest Court Report Format As A Podcast 111. Land and Sites Required Nationwide 112. Late August 2026 Advertised Requirements 113. Podcast Update: September 2026 114. Samsung Telly’s 115. Unpaid, Late, and Aged Debt from 01/07/2022 116. Mini Update From Court Guinness 117. Updated Site List June 2021 118. Retro Tech: Borland Sidekick Vs Symantec Act! 119. Podcast: Retro Road Test: 1994 Porsche 928 GTS vs Jaguar XJS 4.0 & V12 120. Retro Road test: Toyota Land Cruiser vs Mitsubishi Shogun 121. Podcast: I Wonder What Is Happening In Retail 122. The Latest News From Court 123. Olympics Planning 124. Press Release 125. The Surrey Special Edition Slideshare #2024 #blog #bulletin #community #courtGuinness #news #report #september2024 #topPosts #writing -
Just what I needed. A company executive using a video from a #polarizing and #divisive figure in the software industry to try to send a message to the company's workers.
I would hope that someone at that level would try to find less #problematic #messenger for the message. Until they do, I think I won't bother with the message.
This executive has #disappointed me several times recently and I hope someone near their level talks to them about some of their poor #choices.
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Just what I needed. A company executive using a video from a #polarizing and #divisive figure in the software industry to try to send a message to the company's workers.
I would hope that someone at that level would try to find less #problematic #messenger for the message. Until they do, I think I won't bother with the message.
This executive has #disappointed me several times recently and I hope someone near their level talks to them about some of their poor #choices.
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This week, the @dnsoarc board and staff gathered for a retreat to shape strategy for the coming years. We outlined plans to enhance workshops and raise the value of our software, services, and research portfolio. These choices also guide our search for a new president, which is progressing nicely.
We look forward to sharing updates at the next workshop in Vancouver.
On a personal note, I think it’s really rewarding to share my experiences running a small non-profit and to find common ingredients to make DNS OARC successful and sustainable.
Many thanks to CIRA for hosting and facilitating this meeting. We finally captured a photo of the full board together. ❤️🍁
-
This week, the @dnsoarc board and staff gathered for a retreat to shape strategy for the coming years. We outlined plans to enhance workshops and raise the value of our software, services, and research portfolio. These choices also guide our search for a new president, which is progressing nicely.
We look forward to sharing updates at the next workshop in Vancouver.
On a personal note, I think it’s really rewarding to share my experiences running a small non-profit and to find common ingredients to make DNS OARC successful and sustainable.
Many thanks to CIRA for hosting and facilitating this meeting. We finally captured a photo of the full board together. ❤️🍁
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This week, the @dnsoarc board and staff gathered for a retreat to shape strategy for the coming years. We outlined plans to enhance workshops and raise the value of our software, services, and research portfolio. These choices also guide our search for a new president, which is progressing nicely.
We look forward to sharing updates at the next workshop in Vancouver.
On a personal note, I think it’s really rewarding to share my experiences running a small non-profit and to find common ingredients to make DNS OARC successful and sustainable.
Many thanks to CIRA for hosting and facilitating this meeting. We finally captured a photo of the full board together. ❤️🍁
-
This week, the @dnsoarc board and staff gathered for a retreat to shape strategy for the coming years. We outlined plans to enhance workshops and raise the value of our software, services, and research portfolio. These choices also guide our search for a new president, which is progressing nicely.
We look forward to sharing updates at the next workshop in Vancouver.
On a personal note, I think it’s really rewarding to share my experiences running a small non-profit and to find common ingredients to make DNS OARC successful and sustainable.
Many thanks to CIRA for hosting and facilitating this meeting. We finally captured a photo of the full board together. ❤️🍁
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MILK Protocol: A Predictive Kinematic Intelligence Framework for Autonomous Reality-State Modification
Author: pasjrwoctx👽
Concept Proposal by S*A*R*A*H Research InitiativeVersion: 1.0
Field: Robotics, Cybernetics, Autonomous Systems, Control Theory, Digital Twins, AIThis paper introduces the Mechanized Intelligence Link Kinematically (MILK) Protocol, a generalized framework for autonomous systems that continuously model, predict, simulate, and modify physical environments through intelligent kinetic action.
Click to view full article
Unlike traditional control architectures that optimize isolated actions, MILK treats every motion as a state-transforming event within a dynamic reality model. The protocol combines sensor fusion, predictive world modeling, digital-twin simulation, model predictive control (MPC), and machine learning into a unified architecture.
MILK defines quantitative metrics for measuring the influence of actions on future world states, enabling intelligent agents to maximize desired outcomes while minimizing uncertainty, energy expenditure, and risk.
A prototype implementation using a mobile robotic platform demonstrates how MILK can be experimentally validated under real-world conditions.
Keywords: #cybernetics, #robotics, #autonomoussystems, #digitaltwins, #worldmodels, #predictiveintelligence, #human-machineinteraction1. Introduction Modern autonomous systems react to environments. MILK proposes a stronger paradigm: Every action is selected according to its projected influence on future reality states. The protocol assumes: 1. Every kinetic action produces measurable state transitions. 2. Future states can be estimated probabilistically. 3. Better predictions yield better interventions. 4. An autonomous agent should optimize future-state outcomes rather than immediate responses. This creates a closed-loop architecture capable of continuously shaping environments toward desired objectives. 2. Theoretical Foundation Let a system state be represented as: StS_tSt​ where: • StS_tSt​ = complete observable state at time t. An action: AtA_tAt​ produces a transition: St+1S_{t+1}St+1​ such that: St+1=f(St,At,Et)S_{t+1}=f(S_t,A_t,E_t)St+1​=f(St​,At​,Et​) where: • EtE_tEt​ represents environmental factors. 3. MILK Dynamic Equation The original conceptual equation: A+B(1/C)=XA + B(1/C)=XA+B(1/C)=X is formalized as: Xt=At+KtUtX_t=A_t+\frac{K_t}{U_t}Xt​=At​+Ut​Kt​​ where: Variable Meaning Aâ‚œ Intended action vector Kâ‚œ Environmental coupling factor Uâ‚œ Uncertainty score Xâ‚œ Predicted state change Interpretation: • Strong environmental knowledge increases precision. • Higher uncertainty reduces influence prediction accuracy. • Outcome estimates improve as uncertainty approaches zero. 4. Reality-State Modification Index MILK introduces: Reality Modification Index (RMI) RMI=∣∣Sfuture−Scurrent∣∣RMI=||S_{future}-S_{current}||RMI=∣∣Sfuture​−Scurrent​∣∣ Where: • large values indicate substantial environmental change. • small values indicate minimal influence. Examples: Action Approximate RMI Pick up object Low Open door Low Rearrange room Medium Coordinate factory robots High Optimize city traffic Very High The RMI provides a measurable definition of "reality alteration." 5. Architecture MILK consists of five primary layers. Layer 1: Perception Inputs: • Cameras • LiDAR • IMU • Microphones • Tactile sensors • GPS Outputs: WtW_tWt​ Current world model. Layer 2: World Construction Sensor fusion constructs: Wt={Objects,Humans,Locations,Conditions}W_t = \{Objects,Humans,Locations,Conditions\}Wt​={Objects,Humans,Locations,Conditions} Methods: • SLAM • Kalman filters • Bayesian estimation Layer 3: Predictive Simulation Generate: Wt+1,Wt+2,...,Wt+nW_{t+1},W_{t+2},...,W_{t+n}Wt+1​,Wt+2​,...,Wt+n​ using: • Transformer world models • Reinforcement learning • Physics simulation • Digital twins Layer 4: Kinematic Optimization Find optimal action sequence: A∗=argmin(J)A^*=argmin(J)A∗=argmin(J) where J=Error+Risk+Energy+TimeJ=Error+Risk+Energy+TimeJ=Error+Risk+Energy+Time Layer 5: Reality Verification After action execution: Error=Sactual−SpredictedError=S_{actual}-S_{predicted}Error=Sactual​−Spredicted​ Model updates: Modelnew=Modelold+Learning(Error)Model_{new}=Model_{old}+Learning(Error)Modelnew​=Modelold​+Learning(Error) 6. SARAH Autonomous Agent SARAH (Simulated Augmented Reality Assistant Human) is defined as a humanoid embodiment of MILK. Core modules: Self Localization Maintains position estimate. Predictive Cognition Simulates future states. Adaptive Learning Updates behavior from errors. Reality Synchronization Engine Maintains consistency between: • Model • Prediction • Observation 7. Experimental Hypothesis Hypothesis: A MILK-controlled robot will produce significantly lower state-transition error than a conventional reactive controller. Independent Variable: • Control architecture Dependent Variables: • Path accuracy • Task completion rate • Energy consumption • Prediction accuracy • RMI efficiency 8. Testable Prototype Design Prototype Name MILK-P1 Hardware Compute • NVIDIA Jetson Orin Nano • Raspberry Pi 5 Sensors • Intel RealSense D455 • 9-axis IMU • Wheel encoders • Microphone array Mobility • Differential drive robot base Optional • 4 DOF robotic arm Estimated cost: $800-$2500 Software Stack Operating System Ubuntu 24.04 Middleware ROS2 Vision OpenCV AI PyTorch Simulation Gazebo Digital Twin NVIDIA Isaac Sim 9. Experimental Environment Construct a room containing: • Chairs • Boxes • Doors • Human participants Robot objective: Navigate from Point A to Point B while: • avoiding obstacles • responding to environmental changes • predicting future movement of agents 10. Test Sequence Trial 1 Reactive Controller Robot responds only after detecting changes. Measure: • collisions • errors • time Trial 2 MILK Controller Robot predicts: • moving obstacles • human paths • object displacement before motion occurs. Measure: • prediction accuracy • RMI • completion time 11. Performance Metrics Predictive Accuracy PA=1−∣Predicted−Actual∣PA=1-|Predicted-Actual|PA=1−∣Predicted−Actual∣ Reality Modification Efficiency RME=DesiredStateChangeEnergyUsedRME=\frac{DesiredStateChange}{EnergyUsed}RME=EnergyUsedDesiredStateChange​ State Synchronization Error SSE=∣Sactual−Spredicted∣SSE=|S_{actual}-S_{predicted}|SSE=∣Sactual​−Spredicted​∣ Autonomous Intelligence Score AIS=PA×RMESSEAIS=\frac{PA \times RME}{SSE}AIS=SSEPA×RME​ Higher is better. 12. Expected Outcomes MILK should demonstrate: • Reduced path planning errors • Better obstacle avoidance • Lower energy expenditure • More accurate future-state predictions • Improved adaptation to dynamic environments 13. Future Development MILK-P2: • Full humanoid embodiment • Whole-body control • Multi-agent coordination MILK-P3: • Swarm intelligence • Distributed digital twins • Cloud synchronization MILK-P4: • Human cognitive state modeling • Intent prediction • Collaborative decision systems Conclusion The MILK Protocol transforms the philosophical concept of "reality alteration" into a measurable engineering framework based on state-space control, predictive simulation, digital twins, and autonomous learning. Rather than altering reality in a supernatural sense, MILK quantifies how intelligent actions reshape future physical states and provides a mathematical basis for designing systems, such as SARAH, that can optimize those state transitions with increasing precision. The proposed MILK-P1 prototype is immediately testable using existing robotics hardware and modern AI infrastructure, making the protocol falsifiable, measurable, and suitable for academic research and experimental validation.
Click to view code#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ MILK Protocol v1.0 -- Reference Implementation ============================================== A Predictive Kinematic Intelligence Framework for Autonomous Reality-State Modification. This module implements the architecture described in: "MILK Protocol: A Predictive Kinematic Intelligence Framework for Autonomous Reality-State Modification" Concept Proposal by SARAH Research Initiative, v1.0 Contents -------- Layer 1 PerceptionLayer -- sensors -> SensorFrame Layer 2 WorldModel -- sensor fusion -> W_t (tracked objects) Layer 3 PredictiveSimulator -- W_t -> W_{t+1} .. W_{t+n} Layer 4 KinematicOptimizer -- argmin J = Error+Risk+Energy+Time Layer 5 RealityVerifier -- SSE -> model adaptation Math milk_dynamic_equation -- X_t = A_t + K_t / U_t Metric reality_modification_index (RMI), PA, RME, SSE, AIS Agent SARAH -- humanoid embodiment of MILK Run: python milk_protocol.py --help python milk_protocol.py --demo # single rendered episode python milk_protocol.py --trials 5 # full experiment python milk_protocol.py --selftest # unit checks Dependencies: numpy only. """ from __future__ import annotations import argparse import json import math import sys import time from dataclasses import dataclass, field, asdict from typing import Dict, List, Optional, Sequence, Tuple import numpy as np # ===================================================================== # 0. Utilities # ===================================================================== EPS = 1e-9 def wrap_angle(a: float) -> float: """Wrap an angle to (-pi, pi].""" return (a + math.pi) % (2.0 * math.pi) - math.pi def integrate_diff_drive(pose: np.ndarray, vel: np.ndarray, action: np.ndarray, cfg: "MILKConfig") -> Tuple[np.ndarray, np.ndarray]: """ Shared differential-drive integrator used by BOTH the environment and the predictive simulator. Keeping them identical means any residual prediction error comes from sensing noise / unmodelled slip, not from a model mismatch -- which is exactly what the Reality Verification layer (Layer 5) is supposed to measure. pose : (x, y, theta) vel : (v, omega) action : (v_cmd, omega_cmd) -- rate limited by a_max / alpha_max """ dt = cfg.dt v = float(vel[0]) + float(np.clip(action[0] - vel[0], -cfg.a_max * dt, cfg.a_max * dt)) w = float(vel[1]) + float(np.clip(action[1] - vel[1], -cfg.alpha_max * dt, cfg.alpha_max * dt)) x = float(pose[0]) + v * math.cos(pose[2]) * dt y = float(pose[1]) + v * math.sin(pose[2]) * dt th = wrap_angle(float(pose[2]) + w * dt) return np.array([x, y, th]), np.array([v, w]) def ray_circle(ox: float, oy: float, dx: float, dy: float, cx: float, cy: float, r: float) -> float: """Distance along unit ray (dx,dy) from (ox,oy) to circle, or inf.""" fx, fy = ox - cx, oy - cy b = 2.0 * (fx * dx + fy * dy) c = fx * fx + fy * fy - r * r disc = b * b - 4.0 * c if disc < 0.0: return float("inf") sq = math.sqrt(disc) t1 = (-b - sq) / 2.0 t2 = (-b + sq) / 2.0 if t1 > 1e-6: return t1 if t2 > 1e-6: return t2 return float("inf") # ===================================================================== # 1. Configuration # ===================================================================== @dataclass class MILKConfig: """All tunable parameters of the MILK stack.""" # -- timing ------------------------------------------------------- dt: float = 0.10 horizon: int = 12 # -- robot limits ------------------------------------------------- v_max: float = 1.20 omega_max: float = 1.80 a_max: float = 2.00 alpha_max: float = 4.00 robot_radius: float = 0.22 # -- sensor model ------------------------------------------------- sensor_range: float = 6.00 n_rays: int = 72 range_sigma: float = 0.020 gps_sigma: float = 0.040 compass_sigma: float = 0.030 encoder_sigma: float = 0.020 gyro_sigma: float = 0.030 detect_sigma: float = 0.080 p_detect: float = 0.92 # -- environment process noise (wheel slip, unmodelled dynamics) -- slip_v: float = 0.020 slip_w: float = 0.030 # -- MILK dynamic equation --------------------------------------- u_min: float = 1e-3 # floor on uncertainty (avoids K/U blow-up) # -- optimizer weights (J = Error + Risk + Energy + Time) -------- w_error: float = 1.00 w_risk: float = 6.00 w_energy: float = 0.20 w_time: float = 0.05 w_smooth: float = 0.30 # -- optimizer sampling ------------------------------------------- n_v_samples: int = 5 n_w_samples: int = 11 n_random: int = 60 # -- world model --------------------------------------------------- track_q: float = 0.35 # KF process noise track_timeout: int = 8 # frames before a track is dropped # -- metrics ------------------------------------------------------- w_sse_pose: float = 1.00 w_sse_obj: float = 1.00 sse_scale: float = 0.50 # normalisation for Predictive Accuracy # -- misc ---------------------------------------------------------- seed: int = 0 # ===================================================================== # 2. Environment (the "real world") # ===================================================================== @dataclass class Circle: x: float y: float r: float @dataclass class Human: id: int x: float y: float vx: float vy: float r: float = 0.30 class RoomEnvironment: """ Ground-truth simulator. A rectangular room containing static circular obstacles and moving humans. The robot is a differential-drive base. Nothing in this class is visible to the controllers except through the PerceptionLayer. """ def __init__(self, cfg: MILKConfig, seed: int = 0, n_static: int = 6, n_humans: int = 2): self.cfg = cfg self.rng = np.random.default_rng(seed) self.width = 10.0 self.height = 8.0 self.start = np.array([1.0, 1.0, 0.0]) self.goal = np.array([self.width - 1.0, self.height - 1.0]) self.robot_pose = self.start.copy() self.robot_vel = np.zeros(2) self.static: List[Circle] = [] self._build_static(n_static) self.humans: List[Human] = [] self._build_humans(n_humans) self.t = 0.0 self.collision_events = 0 self._in_collision = False # ------------------------------------------------------------------ def _build_static(self, n: int) -> None: tries = 0 while len(self.static) < n and tries < 2000: tries += 1 r = float(self.rng.uniform(0.30, 0.60)) x = float(self.rng.uniform(r + 0.3, self.width - r - 0.3)) y = float(self.rng.uniform(r + 0.3, self.height - r - 0.3)) if math.hypot(x - self.start[0], y - self.start[1]) < 1.4: continue if math.hypot(x - self.goal[0], y - self.goal[1]) < 1.4: continue if any(math.hypot(x - c.x, y - c.y) < r + c.r + 0.7 for c in self.static): continue self.static.append(Circle(x, y, r)) def _build_humans(self, n: int) -> None: for i in range(n): x = float(self.rng.uniform(2.0, self.width - 2.0)) y = float(self.rng.uniform(2.0, self.height - 2.0)) ang = float(self.rng.uniform(0, 2 * math.pi)) sp = float(self.rng.uniform(0.25, 0.55)) self.humans.append(Human(i, x, y, sp * math.cos(ang), sp * math.sin(ang))) # ------------------------------------------------------------------ # Kinematics / dynamics # ------------------------------------------------------------------ def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """Advance the world by one dt. Returns (new_pose, new_vel).""" cfg = self.cfg new_pose, new_vel = integrate_diff_drive(self.robot_pose, self.robot_vel, action, cfg) # unmodelled slip / process noise -- this is what makes prediction hard new_vel = new_vel + self.rng.normal(0.0, [cfg.slip_v, cfg.slip_w]) new_pose[2] = wrap_angle(new_pose[2] + self.rng.normal(0.0, 0.01)) self.robot_pose = new_pose self.robot_vel = new_vel self._step_humans(cfg.dt) self.t += cfg.dt # collision bookkeeping hit = self.check_collision() if hit and not self._in_collision: self.collision_events += 1 self._in_collision = hit return self.robot_pose.copy(), self.robot_vel.copy() def _step_humans(self, dt: float) -> None: for h in self.humans: h.vx += float(self.rng.normal(0.0, 0.25)) * dt h.vy += float(self.rng.normal(0.0, 0.25)) * dt sp = math.hypot(h.vx, h.vy) if sp > 0.85: h.vx *= 0.85 / sp h.vy *= 0.85 / sp h.x += h.vx * dt h.y += h.vy * dt if h.x < h.r: h.x = h.r h.vx = abs(h.vx) elif h.x > self.width - h.r: h.x = self.width - h.r h.vx = -abs(h.vx) if h.y < h.r: h.y = h.r h.vy = abs(h.vy) elif h.y > self.height - h.r: h.y = self.height - h.r h.vy = -abs(h.vy) # ------------------------------------------------------------------ # Sensing primitives (used by the PerceptionLayer) # ------------------------------------------------------------------ def raycast(self, pose: np.ndarray, angles: np.ndarray) -> np.ndarray: """Ideal (noise-free) range readings for a fan of rays.""" ox, oy, oth = float(pose[0]), float(pose[1]), float(pose[2]) rng_max = self.cfg.sensor_range out = np.full(len(angles), rng_max, dtype=float) targets = [(c.x, c.y, c.r) for c in self.static] targets += [(h.x, h.y, h.r) for h in self.humans] for i, a in enumerate(angles): ang = oth + float(a) dx, dy = math.cos(ang), math.sin(ang) t = self._wall_distance(ox, oy, dx, dy) for (cx, cy, cr) in targets: tc = ray_circle(ox, oy, dx, dy, cx, cy, cr) if tc < t: t = tc out[i] = min(t, rng_max) return out def _wall_distance(self, ox: float, oy: float, dx: float, dy: float) -> float: ts = [] if dx > EPS: ts.append((self.width - ox) / dx) elif dx < -EPS: ts.append((0.0 - ox) / dx) if dy > EPS: ts.append((self.height - oy) / dy) elif dy < -EPS: ts.append((0.0 - oy) / dy) ts = [t for t in ts if t > EPS] return min(ts) if ts else float("inf") def check_collision(self) -> bool: rx, ry = float(self.robot_pose[0]), float(self.robot_pose[1]) rr = self.cfg.robot_radius for c in self.static: if math.hypot(rx - c.x, ry - c.y) < rr + c.r: return True for h in self.humans: if math.hypot(rx - h.x, ry - h.y) < rr + h.r: return True if rx < rr or rx > self.width - rr or ry < rr or ry > self.height - rr: return True return False # ------------------------------------------------------------------ def ground_truth(self) -> Dict: """The state the controller is trying to predict.""" return { "pose": self.robot_pose.copy(), "vel": self.robot_vel.copy(), "objects": {h.id: np.array([h.x, h.y]) for h in self.humans}, } def goal_distance(self) -> float: return float(np.linalg.norm(self.robot_pose[:2] - self.goal)) # ===================================================================== # 3. Layer 1 -- Perception # ===================================================================== @dataclass class SensorFrame: t: float dt: float angles: np.ndarray ranges: np.ndarray gps_xy: np.ndarray compass_theta: float encoder_v: float gyro_w: float detections: Dict[int, np.ndarray] # object id -> noisy (x, y) class PerceptionLayer: """Layer 1: raw, noisy, partial observation of the world.""" def __init__(self, cfg: MILKConfig, seed: int = 0): self.cfg = cfg self.rng = np.random.default_rng(seed + 1234) self.angles = np.linspace(-math.pi, math.pi, cfg.n_rays, endpoint=False) def sense(self, env: RoomEnvironment) -> SensorFrame: cfg = self.cfg pose = env.robot_pose # --- LiDAR / depth ------------------------------------------- ranges = env.raycast(pose, self.angles) ranges = np.clip(ranges + self.rng.normal(0.0, cfg.range_sigma, ranges.shape), 0.0, cfg.sensor_range) # --- GPS ------------------------------------------------------ gps = pose[:2] + self.rng.normal(0.0, cfg.gps_sigma, 2) # --- IMU / compass ------------------------------------------- compass = wrap_angle(pose[2] + float(self.rng.normal(0.0, cfg.compass_sigma))) gyro = float(env.robot_vel[1] + self.rng.normal(0.0, cfg.gyro_sigma)) # --- wheel encoders ------------------------------------------ enc = float(env.robot_vel[0] + self.rng.normal(0.0, cfg.encoder_sigma)) # --- object detector (people / dynamic agents) --------------- detections: Dict[int, np.ndarray] = {} for h in env.humans: d = math.hypot(h.x - pose[0], h.y - pose[1]) if d > cfg.sensor_range: continue if self.rng.random() > cfg.p_detect: continue z = np.array([h.x, h.y]) + self.rng.normal(0.0, cfg.detect_sigma, 2) detections[h.id] = z return SensorFrame( t=env.t, dt=cfg.dt, angles=self.angles, ranges=ranges, gps_xy=gps, compass_theta=compass, encoder_v=enc, gyro_w=gyro, detections=detections, ) # ===================================================================== # 4. Layer 2 -- World Construction (sensor fusion -> W_t) # ===================================================================== class TrackedObject: """Constant-velocity Kalman filter: state = [x, y, vx, vy].""" def __init__(self, oid: int, x: float, y: float, vx: float = 0.0, vy: float = 0.0): self.id = oid self.x = np.array([x, y, vx, vy], dtype=float) self.P = np.diag([0.25, 0.25, 1.00, 1.00]) self.missed = 0 def predict(self, dt: float, q: float) -> None: F = np.array([[1, 0, dt, 0], [0, 1, 0, dt], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) Q = q * np.diag([dt ** 4 / 4.0, dt ** 4 / 4.0, dt ** 2, dt ** 2]) self.x = F @ self.x self.P = F @ self.P @ F.T + Q def update(self, z: np.ndarray, R: np.ndarray) -> None: H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], dtype=float) y = z - H @ self.x S = H @ self.P @ H.T + R K = self.P @ H.T @ np.linalg.inv(S) self.x = self.x + K @ y self.P = (np.eye(4) - K @ H) @ self.P self.missed = 0 # -- convenience --------------------------------------------------- @property def position(self) -> np.ndarray: return self.x[:2].copy() @property def velocity(self) -> np.ndarray: return self.x[2:].copy() @property def pos_var(self) -> float: return float(self.P[0, 0] + self.P[1, 1]) @property def vel_var(self) -> float: return float(self.P[2, 2] + self.P[3, 3]) class WorldModel: """ Layer 2: builds the current world model W_t = { Objects, Humans, Locations, Conditions } from noisy sensor frames using a pose EKF + per-object Kalman filters. """ def __init__(self, cfg: MILKConfig): self.cfg = cfg self.pose = np.zeros(3) self.pose_cov = np.diag([1.0, 1.0, 0.5]) self.vel = np.zeros(2) self.objects: Dict[int, TrackedObject] = {} self.q_scale = 1.0 # adapted by Layer 5 self.initialised = False self.t = 0.0 # ------------------------------------------------------------------ def fuse(self, frame: SensorFrame) -> None: cfg = self.cfg dt = frame.dt if not self.initialised: self.pose = np.array([frame.gps_xy[0], frame.gps_xy[1], frame.compass_theta]) self.initialised = True else: self._predict_pose(dt, frame.encoder_v, frame.gyro_w) # predict all tracks forward to the current instant for o in self.objects.values(): o.predict(dt, cfg.track_q * self.q_scale) # measurement updates self._update_pose(frame.gps_xy, frame.compass_theta) R = np.eye(2) * (cfg.detect_sigma ** 2) for oid, z in frame.detections.items(): if oid in self.objects: self.objects[oid].update(z, R) else: self.objects[oid] = TrackedObject(oid, float(z[0]), float(z[1])) # age out stale tracks dead = [] for oid, o in self.objects.items(): if oid not in frame.detections: o.missed += 1 if o.missed > cfg.track_timeout: dead.append(oid) for oid in dead: del self.objects[oid] self.vel = np.array([frame.encoder_v, frame.gyro_w]) self.t = frame.t # ------------------------------------------------------------------ def _predict_pose(self, dt: float, v: float, w: float) -> None: x, y, th = self.pose F = np.array([[1.0, 0.0, -v * math.sin(th) * dt], [0.0, 1.0, v * math.cos(th) * dt], [0.0, 0.0, 1.0]]) self.pose = np.array([x + v * math.cos(th) * dt, y + v * math.sin(th) * dt, wrap_angle(th + w * dt)]) Q = self.q_scale * np.diag([0.010, 0.010, 0.004]) self.pose_cov = F @ self.pose_cov @ F.T + Q def _update_pose(self, z_xy: np.ndarray, z_th: float) -> None: cfg = self.cfg R = np.diag([cfg.gps_sigma ** 2, cfg.gps_sigma ** 2, cfg.compass_sigma ** 2]) y = np.array([z_xy[0] - self.pose[0], z_xy[1] - self.pose[1], wrap_angle(z_th - self.pose[2])]) S = self.pose_cov + R K = self.pose_cov @ np.linalg.inv(S) self.pose = self.pose + K @ y self.pose[2] = wrap_angle(self.pose[2]) self.pose_cov = (np.eye(3) - K) @ self.pose_cov # ------------------------------------------------------------------ def object_positions(self) -> Dict[int, np.ndarray]: return {oid: o.position for oid, o in self.objects.items()} def localisation_sigma(self) -> float: return math.sqrt(max(0.0, float(self.pose_cov[0, 0] + self.pose_cov[1, 1]))) # ===================================================================== # 5. MILK Mathematics # ===================================================================== @dataclass class MILKInfluence: """Container for the terms of the MILK dynamic equation.""" A: np.ndarray # intended action vector K: float # environmental coupling factor U: float # uncertainty score X: np.ndarray # predicted state change @property def magnitude(self) -> float: return float(np.linalg.norm(self.X)) def as_dict(self) -> Dict: return {"A": self.A.tolist(), "K": self.K, "U": self.U, "X": self.X.tolist(), "|X|": self.magnitude} def milk_dynamic_equation(A, K: float, U: float, u_min: float = 1e-3): """ The MILK dynamic equation: X_t = A_t + K_t / U_t A_t : intended action vector K_t : environmental coupling factor (how strongly the agent's action couples into the environment) U_t : uncertainty score (floored at u_min) NOTE ON NUMERICS ---------------- As U -> 0 the term K/U diverges, exactly as the source document states ("outcome estimates improve as uncertainty approaches zero"). In a physical implementation U is floored at u_min, and X is used as a *relative influence score* -- not as a literal pose delta. """ A = np.asarray(A, dtype=float) U_eff = max(float(U), float(u_min)) return A + (float(K) / U_eff) def _sse_weights(n_objects: int, cfg: MILKConfig) -> np.ndarray: base = np.array([1.0, 1.0, 0.5, # pose (x, y, theta) 0.2, 0.2, # velocity (v, omega) 1.0, 1.0]) # goal obj = np.ones(2 * n_objects) return np.concatenate([base, obj]) def state_vector(pose, vel, goal, objects: Dict[int, np.ndarray]) -> np.ndarray: """ Canonical flat state vector used for RMI / SSE computations. Object ordering is by ascending id so vectors are comparable. """ parts = [np.asarray(pose, float)[:3], np.asarray(vel, float)[:2], np.asarray(goal, float)[:2]] for k in sorted(objects): parts.append(np.asarray(objects[k], float)[:2]) return np.concatenate(parts) def reality_modification_index(s_a: np.ndarray, s_b: np.ndarray, weights: Optional[np.ndarray] = None) -> float: """ Layer metric -- Reality Modification Index: RMI = || S_future - S_current || Large values => substantial environmental change. Small values => minimal influence. """ a = np.asarray(s_a, float) b = np.asarray(s_b, float) n = min(len(a), len(b)) d = b[:n] - a[:n] if weights is not None: d = d * np.asarray(weights, float)[:n] return float(np.linalg.norm(d)) # ===================================================================== # 6. Layer 3 -- Predictive Simulation # ===================================================================== class PredictiveSimulator: """ Layer 3: generate W_{t+1} .. W_{t+n} using the world model, a constant-velocity motion model for dynamic agents, and exact differential-drive kinematics for the ego robot. (A production system would swap this for a transformer world model or a PhysX/Isaac digital twin; the interface stays identical.) """ def __init__(self, cfg: MILKConfig): self.cfg = cfg # ------------------------------------------------------------------ def rollout_robot(self, pose: np.ndarray, vel: np.ndarray, action_seq: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """Roll the ego robot forward under a candidate action sequence.""" cfg = self.cfg traj = np.empty((len(action_seq) + 1, 3), dtype=float) vels = np.empty(len(action_seq), dtype=float) p = np.asarray(pose, float).copy() v = np.asarray(vel, float).copy() traj[0] = p for k in range(len(action_seq)): p, v = integrate_diff_drive(p, v, action_seq[k], cfg) traj[k + 1] = p vels[k] = v[0] return traj, vels # ------------------------------------------------------------------ def predict_objects(self, world: WorldModel) -> Dict[int, Tuple[np.ndarray, np.ndarray, float]]: """ Predict each tracked dynamic object over the horizon. Returns {id: (positions (H+1,2), variances (H+1,), radius)} """ cfg = self.cfg H = cfg.horizon dt = cfg.dt F = np.array([[1, 0, dt, 0], [0, 1, 0, dt], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) Q = cfg.track_q * np.diag([dt ** 4 / 4, dt ** 4 / 4, dt ** 2, dt ** 2]) out: Dict[int, Tuple[np.ndarray, np.ndarray, float]] = {} for oid, obj in world.objects.items(): x = obj.x.copy() P = obj.P.copy() pos = np.empty((H + 1, 2)) var = np.empty(H + 1) pos[0] = x[:2] var[0] = P[0, 0] + P[1, 1] for k in range(H): x = F @ x P = F @ P @ F.T + Q pos[k + 1] = x[:2] var[k + 1] = P[0, 0] + P[1, 1] out[oid] = (pos, var, 0.30) # 0.30 m nominal agent radius return out # ------------------------------------------------------------------ def predict_next_objects(self, world: WorldModel) -> Dict[int, np.ndarray]: """One-step-ahead object prediction (used for the SSE metric).""" preds = self.predict_objects(world) return {oid: v[0][1].copy() for oid, v in preds.items()} # ===================================================================== # 7. Layer 4 -- Kinematic Optimization # ===================================================================== class KinematicOptimizer: """ Layer 4: find the action sequence minimising J = Error + Risk + Energy + Time (+ smoothness regulariser) via sampling-based receding-horizon (MPC) optimisation. """ def __init__(self, cfg: MILKConfig, sim: PredictiveSimulator, seed: int = 0): self.cfg = cfg self.sim = sim self.rng = np.random.default_rng(seed + 99) self.last_cost = float("inf") self.n_evaluated = 0 # ------------------------------------------------------------------ def _candidates(self, prev_action: np.ndarray) -> List[np.ndarray]: cfg = self.cfg H = cfg.horizon cands: List[np.ndarray] = [] vs = np.linspace(0.0, cfg.v_max, cfg.n_v_samples) ws = np.linspace(-cfg.omega_max, cfg.omega_max, cfg.n_w_samples) for v in vs: for w in ws: cands.append(np.tile([v, w], (H, 1))) # a handful of two-phase manoeuvres (turn-then-drive) half = max(1, H // 2) for _ in range(cfg.n_random): v1 = float(self.rng.uniform(0.0, cfg.v_max)) w1 = float(self.rng.uniform(-cfg.omega_max, cfg.omega_max)) v2 = float(self.rng.uniform(0.0, cfg.v_max)) w2 = float(self.rng.uniform(-cfg.omega_max, cfg.omega_max)) seq = np.vstack([np.tile([v1, w1], (half, 1)), np.tile([v2, w2], (H - half, 1))]) cands.append(seq) # always include "brake hard" cands.append(np.tile([0.0, 0.0], (H, 1))) return cands # ------------------------------------------------------------------ def _cost(self, traj: np.ndarray, vels: np.ndarray, goal: np.ndarray, pred_objs: Dict[int, Tuple[np.ndarray, np.ndarray, float]], action_seq: np.ndarray, prev_action: np.ndarray, bounds: Tuple[float, float, float, float], risk_gain: float) -> float: cfg = self.cfg dt = cfg.dt H = len(action_seq) # ---- Error : terminal distance + heading misalignment -------- final = traj[-1] d_goal = float(np.linalg.norm(final[:2] - goal)) desired = math.atan2(goal[1] - final[1], goal[0] - final[0]) head_err = abs(wrap_angle(desired - final[2])) error = d_goal + 0.25 * head_err # ---- Risk : predicted collision exposure --------------------- risk = 0.0 for oid, (pos, var, orad) in pred_objs.items(): n = min(len(pos), len(traj)) d = np.linalg.norm(pos[:n] - traj[:n, :2], axis=1) clearance = d - (cfg.robot_radius + orad) sigma = np.sqrt(var[:n]) + 0.15 risk += float(np.sum(np.exp(-np.maximum(clearance, 0.0) ** 2 / (2.0 * sigma ** 2)))) risk += 100.0 * float(np.sum(clearance < 0.0)) # ---- wall risk ------------------------------------------------ x0, x1, y0, y1 = bounds margin = cfg.robot_radius + 0.05 outside = ((traj[:, 0] < x0 + margin) | (traj[:, 0] > x1 - margin) | (traj[:, 1] < y0 + margin) | (traj[:, 1] > y1 - margin)) wall_risk = 100.0 * float(np.sum(outside)) # ---- Energy --------------------------------------------------- w_cmd = action_seq[:, 1] energy = float(np.sum(vels ** 2 + 0.30 * w_cmd ** 2) * dt) # ---- Time : expected remaining time to goal ------------------- v_avg = max(float(np.mean(np.abs(vels))), 0.20) time_term = d_goal / v_avg # ---- Smoothness ---------------------------------------------- smooth = float(np.linalg.norm(action_seq[0] - prev_action)) return (cfg.w_error * error + cfg.w_risk * risk_gain * (risk + wall_risk) + cfg.w_energy * energy + cfg.w_time * time_term + cfg.w_smooth * smooth) # ------------------------------------------------------------------ def optimize(self, pose: np.ndarray, vel: np.ndarray, goal: np.ndarray, pred_objs: Dict[int, Tuple[np.ndarray, np.ndarray, float]], prev_action: np.ndarray, bounds: Tuple[float, float, float, float], risk_gain: float = 1.0): """Returns (best_action, info_dict).""" best_seq = None best_cost = float("inf") best_traj = None best_vels = None for seq in self._candidates(prev_action): traj, vels = self.sim.rollout_robot(pose, vel, seq) c = self._cost(traj, vels, goal, pred_objs, seq, prev_action, bounds, risk_gain) if c < best_cost: best_cost = c best_seq = seq best_traj = traj best_vels = vels self.last_cost = best_cost self.n_evaluated += 1 info = { "cost": best_cost, "trajectory": best_traj, "vels": best_vels, "sequence": best_seq, } return best_seq[0].copy(), info # ===================================================================== # 8. Layer 5 -- Reality Verification # ===================================================================== class RealityVerifier: """ Layer 5: compare predicted vs. actual state and adapt the world model. Error = S_actual - S_predicted Model_new = Model_old + Learning(Error) Adaptation here adjusts the Kalman process-noise scale: persistent under-prediction of motion raises q, persistent over-prediction lowers it. """ def __init__(self, cfg: MILKConfig): self.cfg = cfg self.history: List[float] = [] self.q_scale = 1.0 self.lr = 0.08 self.target = 0.08 def verify(self, error: float) -> float: self.history.append(float(error)) return float(error) def learn(self) -> float: if not self.history: return self.q_scale e = self.history[-1] self.q_scale *= (1.0 + self.lr * (e - self.target)) self.q_scale = float(np.clip(self.q_scale, 0.25, 8.0)) return self.q_scale def mean_error(self) -> float: return float(np.mean(self.history)) if self.history else 0.0 def prediction_error(pred: Dict, gt: Dict, cfg: MILKConfig) -> Tuple[float, float, float]: """ Compute the State Synchronization Error between a prediction snapshot and ground truth. SSE = w_pose * ||pose_pred - pose_actual|| + w_obj * mean ||obj_pred - obj_actual|| Returns (sse_total, pose_error, object_error) """ p_pose = np.asarray(pred["pose"], float) a_pose = np.asarray(gt["pose"], float) e_pose = float(np.linalg.norm(p_pose[:2] - a_pose[:2])) e_objs = [] for oid, p in pred.get("objects", {}).items(): if oid in gt["objects"]: e_objs.append(float(np.linalg.norm(np.asarray(p, float)[:2] - np.asarray(gt["objects"][oid], float)[:2]))) e_obj = float(np.mean(e_objs)) if e_objs else 0.0 total = cfg.w_sse_pose * e_pose + cfg.w_sse_obj * e_obj return total, e_pose, e_obj # ===================================================================== # 9. SARAH -- Simulated Augmented Reality Assistant Human # ===================================================================== class SelfLocalizationModule: """Maintains the position estimate of the embodiment.""" def __init__(self, world: WorldModel): self.world = world @property def pose(self) -> np.ndarray: return self.world.pose @property def covariance(self) -> np.ndarray: return self.world.pose_cov def report(self) -> Dict: return { "pose": self.world.pose.tolist(), "sigma": self.world.localisation_sigma(), } class PredictiveCognitionModule: """Simulates future states of self and others.""" def __init__(self, sim: PredictiveSimulator): self.sim = sim def simulate_self(self, pose, vel, action_seq): return self.sim.rollout_robot(pose, vel, action_seq) def simulate_others(self, world: WorldModel): return self.sim.predict_objects(world) class AdaptiveLearningModule: """Updates behaviour from observed errors.""" def __init__(self, verifier: RealityVerifier): self.verifier = verifier def learn(self) -> float: return self.verifier.learn() def report(self) -> Dict: return {"q_scale": self.verifier.q_scale, "mean_sse": self.verifier.mean_error(), "n": len(self.verifier.history)} class RealitySynchronizationEngine: """ Keeps Model / Prediction / Observation mutually consistent and flags divergence. """ def __init__(self, tol: float = 0.25): self.tol = tol self.log: List[Dict] = [] def synchronize(self, model_pose, predicted_pose, observed_pose) -> Dict: mp = np.asarray(model_pose, float)[:2] pp = np.asarray(predicted_pose, float)[:2] op = np.asarray(observed_pose, float)[:2] rec = { "model_prediction": float(np.linalg.norm(mp - pp)), "prediction_observation": float(np.linalg.norm(pp - op)), "model_observation": float(np.linalg.norm(mp - op)), } rec["synchronized"] = bool(rec["prediction_observation"] < self.tol) self.log.append(rec) return rec def sync_rate(self) -> float: if not self.log: return 0.0 return float(np.mean([r["synchronized"] for r in self.log])) # ===================================================================== # 10. Controllers # ===================================================================== class BaseController: """Common perception + world-model plumbing.""" name = "BASE" def __init__(self, cfg: MILKConfig, seed: int = 0): self.cfg = cfg self.seed = seed self.perception = PerceptionLayer(cfg, seed) self.world = WorldModel(cfg) self.prev_action = np.zeros(2) self.predicted_pose = np.zeros(3) self.predicted_objects: Dict[int, np.ndarray] = {} self.last_frame: Optional[SensorFrame] = None # -- to be overridden --------------------------------------------- def act(self, goal: np.ndarray) -> np.ndarray: raise NotImplementedError def verify(self, pred: Dict, gt: Dict) -> float: return 0.0 # ------------------------------------------------------------------ def observe(self, env: RoomEnvironment) -> None: frame = self.perception.sense(env) self.last_frame = frame self.world.fuse(frame) def prediction_snapshot(self) -> Dict: return {"pose": self.predicted_pose.copy(), "objects": {k: v.copy() for k, v in self.predicted_objects.items()}} def diagnostics(self) -> Dict: return {} # --------------------------------------------------------------------- class ReactiveController(BaseController): """ Baseline: reacts to the world as it currently is. * heads straight for the goal * turns away from anything currently within a fixed radius * no rollout, no prediction of agent motion, no verification layer Its implicit prediction for the next timestep is "the world stays exactly as I currently estimate it". """ name = "REACTIVE" def __init__(self, cfg: MILKConfig, seed: int = 0): super().__init__(cfg, seed) self.avoid_radius = 1.0 def act(self, goal: np.ndarray) -> np.ndarray: cfg = self.cfg pose = self.world.pose # --- pure pursuit --------------------------------------------- desired = math.atan2(goal[1] - pose[1], goal[0] - pose[0]) err = wrap_angle(desired - pose[2]) omega = float(np.clip(2.0 * err, -cfg.omega_max, cfg.omega_max)) v = cfg.v_max * max(0.0, 1.0 - abs(err) / 1.4) # --- reflexive obstacle avoidance ----------------------------- for obj in self.world.objects.values(): rel = obj.position - pose[:2] d = float(np.linalg.norm(rel)) if d > self.avoid_radius: continue bearing = wrap_angle(math.atan2(rel[1], rel[0]) - pose[2]) if abs(bearing) < 0.8: omega = -math.copysign(cfg.omega_max * 0.85, bearing) v = min(v, 0.12) action = np.array([v, omega]) # --- the reactive "prediction": the world is frozen ------------ self.predicted_pose, _ = integrate_diff_drive(pose, self.world.vel, action, cfg) self.predicted_objects = self.world.object_positions() self.prev_action = action return action # --------------------------------------------------------------------- class MILKController(BaseController): """ Full MILK stack: Layer 1 PerceptionLayer Layer 2 WorldModel Layer 3 PredictiveSimulator Layer 4 KinematicOptimizer Layer 5 RealityVerifier + MILK dynamic equation and RMI bookkeeping """ name = "MILK" def __init__(self, cfg: MILKConfig, seed: int = 0): super().__init__(cfg, seed) self.sim = PredictiveSimulator(cfg) self.optimizer = KinematicOptimizer(cfg, self.sim, seed) self.verifier = RealityVerifier(cfg) self.uncertainty = 1.0 self.coupling = 0.0 self.influence: Optional[MILKInfluence] = None self.rmi = 0.0 self.pred_traj: Optional[np.ndarray] = None self.last_cost = float("inf") # ------------------------------------------------------------------ # Uncertainty (U_t) and environmental coupling (K_t) # ------------------------------------------------------------------ def _compute_uncertainty(self) -> float: """ U_t : scalar uncertainty over the horizon. Combines localisation variance with the propagated position uncertainty of every tracked dynamic object. """ cfg = self.cfg loc = self.world.localisation_sigma() terms = [] for o in self.world.objects.values(): growth = (cfg.horizon * cfg.dt) ** 2 * o.vel_var terms.append(o.pos_var + growth) obj = math.sqrt(float(np.mean(terms))) if terms else 0.0 return float(max(loc + 0.5 * obj, cfg.u_min)) def _compute_coupling(self) -> float: """ K_t : environmental coupling factor in [0, 1]. How strongly the agent's actions can couple into the environment: high when nearby, confidently-tracked objects are present; low in empty, featureless space. """ cfg = self.cfg objs = list(self.world.objects.values()) if not objs: return 0.15 ds = np.array([float(np.linalg.norm(o.position - self.world.pose[:2])) for o in objs]) proximity = float(np.mean(np.exp(-ds / cfg.sensor_range))) confidence = float(np.mean([math.exp(-0.5 * o.pos_var / 0.25) for o in objs])) return float(np.clip(proximity * confidence, 0.0, 1.0)) # ------------------------------------------------------------------ def act(self, goal: np.ndarray) -> np.ndarray: cfg = self.cfg pose = self.world.pose vel = self.world.vel # --- Layer 3 : simulate the future --------------------------- pred_objs = self.sim.predict_objects(self.world) # --- Layer 4 : optimise the action --------------------------- self.uncertainty = self._compute_uncertainty() self.coupling = self._compute_coupling() # Higher uncertainty -> more conservative risk weighting. risk_gain = float(np.clip(1.0 + 0.8 * (self.uncertainty - 0.15), 1.0, 3.0)) bounds = (0.0, 20.0, 0.0, 20.0) # generous; wall cost handles margins action, info = self.optimizer.optimize( pose, vel, goal, pred_objs, self.prev_action, bounds, risk_gain) self.pred_traj = info["trajectory"] self.last_cost = info["cost"] # --- MILK dynamic equation : X_t = A_t + K_t / U_t ----------- A = np.array([action[0] * cfg.dt, action[1] * cfg.dt]) X = milk_dynamic_equation(A, self.coupling, self.uncertainty, cfg.u_min) self.influence = MILKInfluence(A=A, K=self.coupling, U=self.uncertainty, X=X) # --- Reality Modification Index ------------------------------ cur_objs = self.world.object_positions() fut_objs = {oid: v[0][-1] for oid, v in pred_objs.items()} s_now = state_vector(self.world.pose, self.world.vel, goal, cur_objs) s_fut = state_vector(self.pred_traj[-1], [float(info["vels"][-1]), action[1]], goal, fut_objs) n_obj = len(set(cur_objs) | set(fut_objs)) self.rmi = reality_modification_index(s_now, s_fut, _sse_weights(n_obj, cfg)) # --- one-step predictions (for the SSE metric) --------------- self.predicted_pose = self.pred_traj[1].copy() self.predicted_objects = {oid: v[0][1].copy() for oid, v in pred_objs.items()} self.prev_action = action return action # ------------------------------------------------------------------ def verify(self, pred: Dict, gt: Dict) -> float: """Layer 5: measure error and adapt the world model.""" sse, _, _ = prediction_error(pred, gt, self.cfg) self.verifier.verify(sse) self.world.q_scale = self.verifier.learn() return sse def diagnostics(self) -> Dict: return { "U": self.uncertainty, "K": self.coupling, "X_norm": self.influence.magnitude if self.influence else 0.0, "RMI": self.rmi, "cost": self.last_cost, "q_scale": self.world.q_scale, } # --------------------------------------------------------------------- class SARAH: """ SARAH -- Simulated Augmented Reality Assistant Human. The humanoid embodiment of the MILK Protocol. Composes the four named core modules on top of the MILK control stack. """ name = "SARAH/MILK" def __init__(self, cfg: MILKConfig, seed: int = 0): self.cfg = cfg self.engine = MILKController(cfg, seed) # -- the four core modules of SARAH --------------------------- self.self_localization = SelfLocalizationModule(self.engine.world) self.predictive_cognition = PredictiveCognitionModule(self.engine.sim) self.adaptive_learning = AdaptiveLearningModule(self.engine.verifier) self.reality_sync = RealitySynchronizationEngine(tol=0.30) # -- MILK interface ------------------------------------------------ def observe(self, env: RoomEnvironment) -> None: self.engine.observe(env) def act(self, goal: np.ndarray) -> np.ndarray: return self.engine.act(goal) def prediction_snapshot(self) -> Dict: return self.engine.prediction_snapshot() def verify(self, pred: Dict, gt: Dict) -> float: sse = self.engine.verify(pred, gt) self.reality_sync.synchronize(self.engine.world.pose, pred["pose"], gt["pose"]) return sse def diagnostics(self) -> Dict: d = self.engine.diagnostics() d["sync_rate"] = self.reality_sync.sync_rate() return d # ===================================================================== # 11. Metrics # ===================================================================== @dataclass class TrialMetrics: controller: str seed: int success: bool steps: int time_to_goal: float collisions: int path_length: float energy: float mean_sse: float mean_pa: float mean_rmi: float rme: float ais: float final_goal_distance: float def as_row(self) -> str: return (f"{self.controller:<10} | {str(self.success):<5} | " f"{self.collisions:^10} | {self.time_to_goal:^7.2f} | " f"{self.path_length:^11.2f} | {self.energy:^6.2f} | " f"{self.mean_sse:^8.3f} | {self.mean_pa:^7.3f} | " f"{self.mean_rmi:^8.2f} | {self.rme:^6.3f} | {self.ais:^6.2f}") class MetricsRecorder: """Accumulates the performance metrics defined in section 11.""" def __init__(self, label: str, cfg: MILKConfig, start_goal_distance: float, seed: int): self.label = label self.cfg = cfg self.start_goal_distance = float(start_goal_distance) self.seed = seed self.sse: List[float] = [] self.rmi: List[float] = [] self.energy = 0.0 self.path_length = 0.0 self.steps = 0 self.prev_xy: Optional[np.ndarray] = None self.time_to_goal = float("nan") # ------------------------------------------------------------------ def step(self, sse: float, rmi: float, action: np.ndarray, pose_xy: np.ndarray) -> None: cfg = self.cfg self.sse.append(float(sse)) self.rmi.append(float(rmi)) # energy proxy for a differential drive: v^2 + k * omega^2 self.energy += float(action[0] ** 2 + 0.30 * action[1] ** 2) * cfg.dt if self.prev_xy is not None: self.path_length += float(np.linalg.norm(pose_xy - self.prev_xy)) self.prev_xy = np.asarray(pose_xy, float).copy() self.steps += 1 # ------------------------------------------------------------------ def finalize(self, env: RoomEnvironment, success: bool) -> TrialMetrics: cfg = self.cfg mean_sse = float(np.mean(self.sse)) if self.sse else 0.0 mean_rmi = float(np.mean(self.rmi)) if self.rmi else 0.0 # Predictive Accuracy: PA = 1 - |Predicted - Actual| (normalised) mean_pa = float(np.clip(1.0 - mean_sse / cfg.sse_scale, 0.0, 1.0)) # Reality Modification Efficiency: RME = DesiredStateChange / Energy desired_change = max(0.0, self.start_goal_distance - env.goal_distance()) energy = max(self.energy, EPS) rme = desired_change / energy # Autonomous Intelligence Score: AIS = PA * RME / SSE ais = (mean_pa * rme) / max(mean_sse, 1e-4) return TrialMetrics( controller=self.label, seed=self.seed, success=bool(success), steps=self.steps, time_to_goal=self.time_to_goal, collisions=env.collision_events, path_length=self.path_length, energy=self.energy, mean_sse=mean_sse, mean_pa=mean_pa, mean_rmi=mean_rmi, rme=rme, ais=ais, final_goal_distance=env.goal_distance(), ) # ===================================================================== # 12. Terminal renderer # ===================================================================== class ASCIIRenderer: """Minimal top-down visualisation for terminals.""" def __init__(self, env: RoomEnvironment, cols: int = 76, rows: int = 22): self.env = env self.cols = cols self.rows = rows def _cell(self, x: float, y: float) -> Tuple[int, int]: c = int(x / self.env.width * (self.cols - 1)) r = int((1.0 - y / self.env.height) * (self.rows - 1)) return (max(0, min(self.cols - 1, c)), max(0, min(self.rows - 1, r))) def render(self, controller: Optional[BaseController] = None, goal: Optional[np.ndarray] = None, header: str = "") -> str: env = self.env grid = [[" "] * self.cols for _ in range(self.rows)] for c in range(self.cols): grid[0][c] = "-" grid[self.rows - 1][c] = "-" for r in range(self.rows): grid[r][0] = "|" grid[r][self.cols - 1] = "|" # static obstacles for ob in env.static: c0, r0 = self._cell(ob.x, ob.y) grid[r0][c0] = "#" # predicted object positions (MILK only) if controller is not None: for oid, p in controller.predicted_objects.items(): c0, r0 = self._cell(float(p[0]), float(p[1])) if grid[r0][c0] == " ": grid[r0][c0] = "o" # predicted ego trajectory if getattr(controller, "pred_traj", None) is not None: for p in controller.pred_traj[1:]: c0, r0 = self._cell(float(p[0]), float(p[1])) if grid[r0][c0] == " ": grid[r0][c0] = "." # humans (ground truth) for h in env.humans: c0, r0 = self._cell(h.x, h.y) grid[r0][c0] = "H" # goal g = env.goal if goal is None else goal c0, r0 = self._cell(float(g[0]), float(g[1])) grid[r0][c0] = "G" # robot c0, r0 = self._cell(float(env.robot_pose[0]), float(env.robot_pose[1])) grid[r0][c0] = "R" lines = [header] if header else [] lines += ["".join(row) for row in grid] return "\n".join(lines) # ===================================================================== # 13. Trial runner # ===================================================================== def make_controller(kind: str, cfg: MILKConfig, seed: int): kind = kind.upper() if kind in ("REACTIVE", "BASELINE"): return ReactiveController(cfg, seed) if kind in ("MILK", "SARAH"): return SARAH(cfg, seed) raise ValueError(f"Unknown controller kind: {kind}") def run_trial(kind: str, cfg: MILKConfig, seed: int, max_steps: int = 400, render: bool = False, render_every: int = 6, verbose: bool = False) -> TrialMetrics: """Run one episode and return the resulting metrics.""" env = RoomEnvironment(cfg, seed=seed) controller = make_controller(kind, cfg, seed) start_dist = env.goal_distance() rec = MetricsRecorder(controller.name, cfg, start_dist, seed) goal = env.goal success = False renderer = ASCIIRenderer(env) if render else None for step in range(max_steps): controller.observe(env) action = controller.act(goal) # -- snapshot the prediction BEFORE the world moves ----------- pred = controller.prediction_snapshot() # -- execute --------------------------------------------------- env.step(action) # -- ground truth AFTER the step ------------------------------- gt = env.ground_truth() sse, e_pose, e_obj = prediction_error(pred, gt, cfg) # -- Layer 5 --------------------------------------------------- controller.verify(pred, gt) # -- bookkeeping ---------------------------------------------- rmi = getattr(controller, "rmi", 0.0) if not isinstance(controller, SARAH): rmi = 0.0 else: rmi = controller.engine.rmi rec.step(sse, rmi, action, env.robot_pose[:2]) if render and (step % render_every == 0): diag = controller.diagnostics() hdr = (f"[{controller.name}] step {step:03d} " f"d_goal={env.goal_distance():5.2f} " f"SSE={sse:.3f} PA={1 - min(sse / cfg.sse_scale, 1):.3f} " f"RMI={rmi:6.2f} " f"U={diag.get('U', 0):.3f} K={diag.get('K', 0):.3f}") print("\033[H\033[J" + renderer.render(controller, goal, hdr)) time.sleep(0.02) # -- termination ---------------------------------------------- if env.goal_distance() < 0.35: success = True rec.time_to_goal = (step + 1) * cfg.dt break if not success: rec.time_to_goal = float("nan") metrics = rec.finalize(env, success) if verbose: print(f" trial seed={seed} {controller.name}: " f"success={success} steps={metrics.steps} " f"SSE={metrics.mean_sse:.3f} PA={metrics.mean_pa:.3f} " f"AIS={metrics.ais:.2f}") return metrics def run_experiment(cfg: MILKConfig, n_trials: int = 3, max_steps: int = 400, verbose: bool = True) -> Dict[str, List[TrialMetrics]]: """Run Trial 1 (reactive) and Trial 2 (MILK) over matched seeds.""" results: Dict[str, List[TrialMetrics]] = {"REACTIVE": [], "SARAH/MILK": []} print("=" * 108) print("MILK PROTOCOL v1.0 -- EXPERIMENTAL VALIDATION") print("=" * 108) for seed in range(n_trials): env_seed = cfg.seed + seed print(f"\n-- Trial pair {seed + 1}/{n_trials} (env seed {env_seed}) --") m1 = run_trial("REACTIVE", cfg, env_seed, max_steps, verbose=verbose) m2 = run_trial("MILK", cfg, env_seed, max_steps, verbose=verbose) results["REACTIVE"].append(m1) results["SARAH/MILK"].append(m2) print("\n" + "=" * 108) print("RESULTS") print("=" * 108) print(f"{'CTRL':<10} | {'OK':<5} | {'COLLISIONS':^10} | {'T[s]':^7} | " f"{'PATH[m]':^11} | {'E':^6} | {'SSE':^8} | {'PA':^7} | " f"{'RMI':^8} | {'RME':^6} | {'AIS':^6}") print("-" * 108) for group in results.values(): for m in group: print(m.as_row()) print("-" * 108) print("\nSUMMARY (mean over trials)") print("-" * 108) for name, group in results.items(): print(f"{name:<12} | " f"success={np.mean([m.success for m in group]):.2f} | " f"collisions={np.mean([m.collisions for m in group]):5.2f} | " f"SSE={np.mean([m.mean_sse for m in group]):.3f} | " f"PA={np.mean([m.mean_pa for m in group]):.3f} | " f"RME={np.mean([m.rme for m in group]):.3f} | " f"AIS={np.mean([m.ais for m in group]):.2f}") # -- hypothesis test ------------------------------------------------ r_sse = np.mean([m.mean_sse for m in results["REACTIVE"]]) m_sse = np.mean([m.mean_sse for m in results["SARAH/MILK"]]) r_col = np.mean([m.collisions for m in results["REACTIVE"]]) m_col = np.mean([m.collisions for m in results["SARAH/MILK"]]) print("\nHYPOTHESIS: MILK produces lower state-transition error than a") print(" conventional reactive controller.") print(f" mean SSE reactive = {r_sse:.4f} MILK = {m_sse:.4f} " f"-> {'SUPPORTED' if m_sse < r_sse else 'NOT SUPPORTED'}") print(f" mean collisions reactive = {r_col:.2f} MILK = {m_col:.2f} " f"-> {'SUPPORTED' if m_col <= r_col else 'NOT SUPPORTED'}") print("=" * 108) return results # ===================================================================== # 14. Self-test # ===================================================================== def selftest() -> bool: """Sanity checks on the core MILK mathematics and components.""" ok = True def check(name: str, cond: bool, detail: str = "") -> None: nonlocal ok status = "PASS" if cond else "FAIL" print(f"[{status}] {name} {detail}") ok = ok and cond cfg = MILKConfig() # --- MILK dynamic equation ---------------------------------------- X = milk_dynamic_equation(np.array([0.1, 0.1]), 0.5, 0.5, cfg.u_min) check("MILK eq. basic", np.allclose(X, [1.1, 1.1]), f"X={X}") X1 = milk_dynamic_equation(np.array([0.0]), 0.5, 0.1, cfg.u_min) X2 = milk_dynamic_equation(np.array([0.0]), 0.5, 0.9, cfg.u_min) check("MILK eq. uncertainty reduces influence", float(X1[0]) > float(X2[0]), f"{X1[0]:.2f} > {X2[0]:.2f}") Xf = milk_dynamic_equation(np.array([0.0]), 0.5, 0.0, cfg.u_min) check("MILK eq. finite at U=0", np.isfinite(Xf[0]), f"X={Xf[0]:.1f}") # --- RMI ------------------------------------------------------------ a = state_vector([0, 0, 0], [0, 0], [1, 1], {0: np.array([2.0, 2.0])}) b = state_vector([0, 0, 0], [0, 0], [1, 1], {0: np.array([2.0, 2.0])}) c = state_vector([3, 4, 0], [0, 0], [1, 1], {0: np.array([2.0, 2.0])}) check("RMI identical states == 0", abs(reality_modification_index(a, b)) < 1e-9) check("RMI grows with displacement", reality_modification_index(a, c) > 4.9) # --- differential drive -------------------------------------------- pose = np.array([0.0, 0.0, 0.0]) vel = np.array([0.0, 0.0]) p1, v1 = integrate_diff_drive(pose, vel, np.array([1.0, 0.0]), cfg) check("diff-drive straight line", abs(p1[0] - 0.02) < 1e-9 and abs(p1[1]) < 1e-9, f"pose={p1}") # --- Kalman filter convergence ------------------------------------- obj = TrackedObject(0, 0.0, 0.0) rng = np.random.default_rng(0) tx, ty = 1.0, 2.0 vx, vy = 0.5, 0.2 for k in range(60): obj.predict(0.1, 0.35) tx += vx * 0.1 ty += vy * 0.1 obj.update(np.array([tx, ty]) + rng.normal(0, 0.08, 2), np.eye(2) * 0.08 ** 2) check("KF converges to true position", float(np.linalg.norm(obj.position - [tx, ty])) < 0.15, f"err={np.linalg.norm(obj.position - [tx,ty]):.4f}") check("KF estimates velocity", float(np.linalg.norm(obj.velocity - [vx, vy])) < 0.20, f"err={np.linalg.norm(obj.velocity - [vx,vy]):.4f}") # --- environment ----------------------------------------------------- env = RoomEnvironment(cfg, seed=1) readings = env.raycast(env.robot_pose, np.linspace(0, 2 * math.pi, 16, endpoint=False)) check("raycast within sensor range", bool(np.all(readings >= 0) and np.all(readings <= cfg.sensor_range + 1e-9))) # --- short smoke run -------------------------------------------------- m = run_trial("MILK", cfg, seed=0, max_steps=60, verbose=False) check("MILK produces finite metrics", math.isfinite(m.mean_sse) and math.isfinite(m.ais)) m2 = run_trial("REACTIVE", cfg, seed=0, max_steps=60, verbose=False) check("Reactive produces finite metrics", math.isfinite(m2.mean_sse) and math.isfinite(m2.ais)) print("\nSELFTEST:", "ALL PASS" if ok else "FAILURES DETECTED") return ok # ===================================================================== # 15. Entry point # ===================================================================== def main() -> int: parser = argparse.ArgumentParser( description="MILK Protocol v1.0 -- reference implementation") parser.add_argument("--trials", type=int, default=3, help="number of trial pairs (default: 3)") parser.add_argument("--seed", type=int, default=0, help="base random seed") parser.add_argument("--steps", type=int, default=400, help="max steps per trial") parser.add_argument("--demo", action="store_true", help="render a single MILK episode in the terminal") parser.add_argument("--render", action="store_true", help="render during the experiment (slow)") parser.add_argument("--controller", type=str, default="MILK", choices=["MILK", "REACTIVE"], help="controller used with --demo") parser.add_argument("--json", type=str, default=None, help="write results to a JSON file") parser.add_argument("--selftest", action="store_true", help="run internal sanity checks and exit") parser.add_argument("--horizon", type=int, default=None, help="override prediction horizon") args = parser.parse_args() cfg = MILKConfig(seed=args.seed) if args.horizon is not None: cfg.horizon = args.horizon if args.selftest: return 0 if selftest() else 1 if args.demo: print(f"Rendering a single episode with the {args.controller} " f"controller. Ctrl-C to stop.\n") run_trial(args.controller, cfg, seed=args.seed, max_steps=args.steps, render=True, render_every=4) return 0 results = run_experiment(cfg, n_trials=args.trials, max_steps=args.steps, verbose=True) if args.json: payload = { name: [asdict(m) for m in group] for name, group in results.items() } with open(args.json, "w") as fh: json.dump(payload, fh, indent=2) print(f"\nWrote {args.json}") return 0 if __name__ == "__main__": sys.exit(main())
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MILK Protocol: A Predictive Kinematic Intelligence Framework for Autonomous Reality-State Modification
Author: pasjrwoctx👽
Concept Proposal by S*A*R*A*H Research InitiativeVersion: 1.0
Field: Robotics, Cybernetics, Autonomous Systems, Control Theory, Digital Twins, AIThis paper introduces the Mechanized Intelligence Link Kinematically (MILK) Protocol, a generalized framework for autonomous systems that continuously model, predict, simulate, and modify physical environments through intelligent kinetic action.
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Unlike traditional control architectures that optimize isolated actions, MILK treats every motion as a state-transforming event within a dynamic reality model. The protocol combines sensor fusion, predictive world modeling, digital-twin simulation, model predictive control (MPC), and machine learning into a unified architecture.
MILK defines quantitative metrics for measuring the influence of actions on future world states, enabling intelligent agents to maximize desired outcomes while minimizing uncertainty, energy expenditure, and risk.
A prototype implementation using a mobile robotic platform demonstrates how MILK can be experimentally validated under real-world conditions.
Keywords: #cybernetics, #robotics, #autonomoussystems, #digitaltwins, #worldmodels, #predictiveintelligence, #human-machineinteraction1. Introduction Modern autonomous systems react to environments. MILK proposes a stronger paradigm: Every action is selected according to its projected influence on future reality states. The protocol assumes: 1. Every kinetic action produces measurable state transitions. 2. Future states can be estimated probabilistically. 3. Better predictions yield better interventions. 4. An autonomous agent should optimize future-state outcomes rather than immediate responses. This creates a closed-loop architecture capable of continuously shaping environments toward desired objectives. 2. Theoretical Foundation Let a system state be represented as: StS_tSt​ where: • StS_tSt​ = complete observable state at time t. An action: AtA_tAt​ produces a transition: St+1S_{t+1}St+1​ such that: St+1=f(St,At,Et)S_{t+1}=f(S_t,A_t,E_t)St+1​=f(St​,At​,Et​) where: • EtE_tEt​ represents environmental factors. 3. MILK Dynamic Equation The original conceptual equation: A+B(1/C)=XA + B(1/C)=XA+B(1/C)=X is formalized as: Xt=At+KtUtX_t=A_t+\frac{K_t}{U_t}Xt​=At​+Ut​Kt​​ where: Variable Meaning Aâ‚œ Intended action vector Kâ‚œ Environmental coupling factor Uâ‚œ Uncertainty score Xâ‚œ Predicted state change Interpretation: • Strong environmental knowledge increases precision. • Higher uncertainty reduces influence prediction accuracy. • Outcome estimates improve as uncertainty approaches zero. 4. Reality-State Modification Index MILK introduces: Reality Modification Index (RMI) RMI=∣∣Sfuture−Scurrent∣∣RMI=||S_{future}-S_{current}||RMI=∣∣Sfuture​−Scurrent​∣∣ Where: • large values indicate substantial environmental change. • small values indicate minimal influence. Examples: Action Approximate RMI Pick up object Low Open door Low Rearrange room Medium Coordinate factory robots High Optimize city traffic Very High The RMI provides a measurable definition of "reality alteration." 5. Architecture MILK consists of five primary layers. Layer 1: Perception Inputs: • Cameras • LiDAR • IMU • Microphones • Tactile sensors • GPS Outputs: WtW_tWt​ Current world model. Layer 2: World Construction Sensor fusion constructs: Wt={Objects,Humans,Locations,Conditions}W_t = \{Objects,Humans,Locations,Conditions\}Wt​={Objects,Humans,Locations,Conditions} Methods: • SLAM • Kalman filters • Bayesian estimation Layer 3: Predictive Simulation Generate: Wt+1,Wt+2,...,Wt+nW_{t+1},W_{t+2},...,W_{t+n}Wt+1​,Wt+2​,...,Wt+n​ using: • Transformer world models • Reinforcement learning • Physics simulation • Digital twins Layer 4: Kinematic Optimization Find optimal action sequence: A∗=argmin(J)A^*=argmin(J)A∗=argmin(J) where J=Error+Risk+Energy+TimeJ=Error+Risk+Energy+TimeJ=Error+Risk+Energy+Time Layer 5: Reality Verification After action execution: Error=Sactual−SpredictedError=S_{actual}-S_{predicted}Error=Sactual​−Spredicted​ Model updates: Modelnew=Modelold+Learning(Error)Model_{new}=Model_{old}+Learning(Error)Modelnew​=Modelold​+Learning(Error) 6. SARAH Autonomous Agent SARAH (Simulated Augmented Reality Assistant Human) is defined as a humanoid embodiment of MILK. Core modules: Self Localization Maintains position estimate. Predictive Cognition Simulates future states. Adaptive Learning Updates behavior from errors. Reality Synchronization Engine Maintains consistency between: • Model • Prediction • Observation 7. Experimental Hypothesis Hypothesis: A MILK-controlled robot will produce significantly lower state-transition error than a conventional reactive controller. Independent Variable: • Control architecture Dependent Variables: • Path accuracy • Task completion rate • Energy consumption • Prediction accuracy • RMI efficiency 8. Testable Prototype Design Prototype Name MILK-P1 Hardware Compute • NVIDIA Jetson Orin Nano • Raspberry Pi 5 Sensors • Intel RealSense D455 • 9-axis IMU • Wheel encoders • Microphone array Mobility • Differential drive robot base Optional • 4 DOF robotic arm Estimated cost: $800-$2500 Software Stack Operating System Ubuntu 24.04 Middleware ROS2 Vision OpenCV AI PyTorch Simulation Gazebo Digital Twin NVIDIA Isaac Sim 9. Experimental Environment Construct a room containing: • Chairs • Boxes • Doors • Human participants Robot objective: Navigate from Point A to Point B while: • avoiding obstacles • responding to environmental changes • predicting future movement of agents 10. Test Sequence Trial 1 Reactive Controller Robot responds only after detecting changes. Measure: • collisions • errors • time Trial 2 MILK Controller Robot predicts: • moving obstacles • human paths • object displacement before motion occurs. Measure: • prediction accuracy • RMI • completion time 11. Performance Metrics Predictive Accuracy PA=1−∣Predicted−Actual∣PA=1-|Predicted-Actual|PA=1−∣Predicted−Actual∣ Reality Modification Efficiency RME=DesiredStateChangeEnergyUsedRME=\frac{DesiredStateChange}{EnergyUsed}RME=EnergyUsedDesiredStateChange​ State Synchronization Error SSE=∣Sactual−Spredicted∣SSE=|S_{actual}-S_{predicted}|SSE=∣Sactual​−Spredicted​∣ Autonomous Intelligence Score AIS=PA×RMESSEAIS=\frac{PA \times RME}{SSE}AIS=SSEPA×RME​ Higher is better. 12. Expected Outcomes MILK should demonstrate: • Reduced path planning errors • Better obstacle avoidance • Lower energy expenditure • More accurate future-state predictions • Improved adaptation to dynamic environments 13. Future Development MILK-P2: • Full humanoid embodiment • Whole-body control • Multi-agent coordination MILK-P3: • Swarm intelligence • Distributed digital twins • Cloud synchronization MILK-P4: • Human cognitive state modeling • Intent prediction • Collaborative decision systems Conclusion The MILK Protocol transforms the philosophical concept of "reality alteration" into a measurable engineering framework based on state-space control, predictive simulation, digital twins, and autonomous learning. Rather than altering reality in a supernatural sense, MILK quantifies how intelligent actions reshape future physical states and provides a mathematical basis for designing systems, such as SARAH, that can optimize those state transitions with increasing precision. The proposed MILK-P1 prototype is immediately testable using existing robotics hardware and modern AI infrastructure, making the protocol falsifiable, measurable, and suitable for academic research and experimental validation.
Click to view code#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ MILK Protocol v1.0 -- Reference Implementation ============================================== A Predictive Kinematic Intelligence Framework for Autonomous Reality-State Modification. This module implements the architecture described in: "MILK Protocol: A Predictive Kinematic Intelligence Framework for Autonomous Reality-State Modification" Concept Proposal by SARAH Research Initiative, v1.0 Contents -------- Layer 1 PerceptionLayer -- sensors -> SensorFrame Layer 2 WorldModel -- sensor fusion -> W_t (tracked objects) Layer 3 PredictiveSimulator -- W_t -> W_{t+1} .. W_{t+n} Layer 4 KinematicOptimizer -- argmin J = Error+Risk+Energy+Time Layer 5 RealityVerifier -- SSE -> model adaptation Math milk_dynamic_equation -- X_t = A_t + K_t / U_t Metric reality_modification_index (RMI), PA, RME, SSE, AIS Agent SARAH -- humanoid embodiment of MILK Run: python milk_protocol.py --help python milk_protocol.py --demo # single rendered episode python milk_protocol.py --trials 5 # full experiment python milk_protocol.py --selftest # unit checks Dependencies: numpy only. """ from __future__ import annotations import argparse import json import math import sys import time from dataclasses import dataclass, field, asdict from typing import Dict, List, Optional, Sequence, Tuple import numpy as np # ===================================================================== # 0. Utilities # ===================================================================== EPS = 1e-9 def wrap_angle(a: float) -> float: """Wrap an angle to (-pi, pi].""" return (a + math.pi) % (2.0 * math.pi) - math.pi def integrate_diff_drive(pose: np.ndarray, vel: np.ndarray, action: np.ndarray, cfg: "MILKConfig") -> Tuple[np.ndarray, np.ndarray]: """ Shared differential-drive integrator used by BOTH the environment and the predictive simulator. Keeping them identical means any residual prediction error comes from sensing noise / unmodelled slip, not from a model mismatch -- which is exactly what the Reality Verification layer (Layer 5) is supposed to measure. pose : (x, y, theta) vel : (v, omega) action : (v_cmd, omega_cmd) -- rate limited by a_max / alpha_max """ dt = cfg.dt v = float(vel[0]) + float(np.clip(action[0] - vel[0], -cfg.a_max * dt, cfg.a_max * dt)) w = float(vel[1]) + float(np.clip(action[1] - vel[1], -cfg.alpha_max * dt, cfg.alpha_max * dt)) x = float(pose[0]) + v * math.cos(pose[2]) * dt y = float(pose[1]) + v * math.sin(pose[2]) * dt th = wrap_angle(float(pose[2]) + w * dt) return np.array([x, y, th]), np.array([v, w]) def ray_circle(ox: float, oy: float, dx: float, dy: float, cx: float, cy: float, r: float) -> float: """Distance along unit ray (dx,dy) from (ox,oy) to circle, or inf.""" fx, fy = ox - cx, oy - cy b = 2.0 * (fx * dx + fy * dy) c = fx * fx + fy * fy - r * r disc = b * b - 4.0 * c if disc < 0.0: return float("inf") sq = math.sqrt(disc) t1 = (-b - sq) / 2.0 t2 = (-b + sq) / 2.0 if t1 > 1e-6: return t1 if t2 > 1e-6: return t2 return float("inf") # ===================================================================== # 1. Configuration # ===================================================================== @dataclass class MILKConfig: """All tunable parameters of the MILK stack.""" # -- timing ------------------------------------------------------- dt: float = 0.10 horizon: int = 12 # -- robot limits ------------------------------------------------- v_max: float = 1.20 omega_max: float = 1.80 a_max: float = 2.00 alpha_max: float = 4.00 robot_radius: float = 0.22 # -- sensor model ------------------------------------------------- sensor_range: float = 6.00 n_rays: int = 72 range_sigma: float = 0.020 gps_sigma: float = 0.040 compass_sigma: float = 0.030 encoder_sigma: float = 0.020 gyro_sigma: float = 0.030 detect_sigma: float = 0.080 p_detect: float = 0.92 # -- environment process noise (wheel slip, unmodelled dynamics) -- slip_v: float = 0.020 slip_w: float = 0.030 # -- MILK dynamic equation --------------------------------------- u_min: float = 1e-3 # floor on uncertainty (avoids K/U blow-up) # -- optimizer weights (J = Error + Risk + Energy + Time) -------- w_error: float = 1.00 w_risk: float = 6.00 w_energy: float = 0.20 w_time: float = 0.05 w_smooth: float = 0.30 # -- optimizer sampling ------------------------------------------- n_v_samples: int = 5 n_w_samples: int = 11 n_random: int = 60 # -- world model --------------------------------------------------- track_q: float = 0.35 # KF process noise track_timeout: int = 8 # frames before a track is dropped # -- metrics ------------------------------------------------------- w_sse_pose: float = 1.00 w_sse_obj: float = 1.00 sse_scale: float = 0.50 # normalisation for Predictive Accuracy # -- misc ---------------------------------------------------------- seed: int = 0 # ===================================================================== # 2. Environment (the "real world") # ===================================================================== @dataclass class Circle: x: float y: float r: float @dataclass class Human: id: int x: float y: float vx: float vy: float r: float = 0.30 class RoomEnvironment: """ Ground-truth simulator. A rectangular room containing static circular obstacles and moving humans. The robot is a differential-drive base. Nothing in this class is visible to the controllers except through the PerceptionLayer. """ def __init__(self, cfg: MILKConfig, seed: int = 0, n_static: int = 6, n_humans: int = 2): self.cfg = cfg self.rng = np.random.default_rng(seed) self.width = 10.0 self.height = 8.0 self.start = np.array([1.0, 1.0, 0.0]) self.goal = np.array([self.width - 1.0, self.height - 1.0]) self.robot_pose = self.start.copy() self.robot_vel = np.zeros(2) self.static: List[Circle] = [] self._build_static(n_static) self.humans: List[Human] = [] self._build_humans(n_humans) self.t = 0.0 self.collision_events = 0 self._in_collision = False # ------------------------------------------------------------------ def _build_static(self, n: int) -> None: tries = 0 while len(self.static) < n and tries < 2000: tries += 1 r = float(self.rng.uniform(0.30, 0.60)) x = float(self.rng.uniform(r + 0.3, self.width - r - 0.3)) y = float(self.rng.uniform(r + 0.3, self.height - r - 0.3)) if math.hypot(x - self.start[0], y - self.start[1]) < 1.4: continue if math.hypot(x - self.goal[0], y - self.goal[1]) < 1.4: continue if any(math.hypot(x - c.x, y - c.y) < r + c.r + 0.7 for c in self.static): continue self.static.append(Circle(x, y, r)) def _build_humans(self, n: int) -> None: for i in range(n): x = float(self.rng.uniform(2.0, self.width - 2.0)) y = float(self.rng.uniform(2.0, self.height - 2.0)) ang = float(self.rng.uniform(0, 2 * math.pi)) sp = float(self.rng.uniform(0.25, 0.55)) self.humans.append(Human(i, x, y, sp * math.cos(ang), sp * math.sin(ang))) # ------------------------------------------------------------------ # Kinematics / dynamics # ------------------------------------------------------------------ def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """Advance the world by one dt. Returns (new_pose, new_vel).""" cfg = self.cfg new_pose, new_vel = integrate_diff_drive(self.robot_pose, self.robot_vel, action, cfg) # unmodelled slip / process noise -- this is what makes prediction hard new_vel = new_vel + self.rng.normal(0.0, [cfg.slip_v, cfg.slip_w]) new_pose[2] = wrap_angle(new_pose[2] + self.rng.normal(0.0, 0.01)) self.robot_pose = new_pose self.robot_vel = new_vel self._step_humans(cfg.dt) self.t += cfg.dt # collision bookkeeping hit = self.check_collision() if hit and not self._in_collision: self.collision_events += 1 self._in_collision = hit return self.robot_pose.copy(), self.robot_vel.copy() def _step_humans(self, dt: float) -> None: for h in self.humans: h.vx += float(self.rng.normal(0.0, 0.25)) * dt h.vy += float(self.rng.normal(0.0, 0.25)) * dt sp = math.hypot(h.vx, h.vy) if sp > 0.85: h.vx *= 0.85 / sp h.vy *= 0.85 / sp h.x += h.vx * dt h.y += h.vy * dt if h.x < h.r: h.x = h.r h.vx = abs(h.vx) elif h.x > self.width - h.r: h.x = self.width - h.r h.vx = -abs(h.vx) if h.y < h.r: h.y = h.r h.vy = abs(h.vy) elif h.y > self.height - h.r: h.y = self.height - h.r h.vy = -abs(h.vy) # ------------------------------------------------------------------ # Sensing primitives (used by the PerceptionLayer) # ------------------------------------------------------------------ def raycast(self, pose: np.ndarray, angles: np.ndarray) -> np.ndarray: """Ideal (noise-free) range readings for a fan of rays.""" ox, oy, oth = float(pose[0]), float(pose[1]), float(pose[2]) rng_max = self.cfg.sensor_range out = np.full(len(angles), rng_max, dtype=float) targets = [(c.x, c.y, c.r) for c in self.static] targets += [(h.x, h.y, h.r) for h in self.humans] for i, a in enumerate(angles): ang = oth + float(a) dx, dy = math.cos(ang), math.sin(ang) t = self._wall_distance(ox, oy, dx, dy) for (cx, cy, cr) in targets: tc = ray_circle(ox, oy, dx, dy, cx, cy, cr) if tc < t: t = tc out[i] = min(t, rng_max) return out def _wall_distance(self, ox: float, oy: float, dx: float, dy: float) -> float: ts = [] if dx > EPS: ts.append((self.width - ox) / dx) elif dx < -EPS: ts.append((0.0 - ox) / dx) if dy > EPS: ts.append((self.height - oy) / dy) elif dy < -EPS: ts.append((0.0 - oy) / dy) ts = [t for t in ts if t > EPS] return min(ts) if ts else float("inf") def check_collision(self) -> bool: rx, ry = float(self.robot_pose[0]), float(self.robot_pose[1]) rr = self.cfg.robot_radius for c in self.static: if math.hypot(rx - c.x, ry - c.y) < rr + c.r: return True for h in self.humans: if math.hypot(rx - h.x, ry - h.y) < rr + h.r: return True if rx < rr or rx > self.width - rr or ry < rr or ry > self.height - rr: return True return False # ------------------------------------------------------------------ def ground_truth(self) -> Dict: """The state the controller is trying to predict.""" return { "pose": self.robot_pose.copy(), "vel": self.robot_vel.copy(), "objects": {h.id: np.array([h.x, h.y]) for h in self.humans}, } def goal_distance(self) -> float: return float(np.linalg.norm(self.robot_pose[:2] - self.goal)) # ===================================================================== # 3. Layer 1 -- Perception # ===================================================================== @dataclass class SensorFrame: t: float dt: float angles: np.ndarray ranges: np.ndarray gps_xy: np.ndarray compass_theta: float encoder_v: float gyro_w: float detections: Dict[int, np.ndarray] # object id -> noisy (x, y) class PerceptionLayer: """Layer 1: raw, noisy, partial observation of the world.""" def __init__(self, cfg: MILKConfig, seed: int = 0): self.cfg = cfg self.rng = np.random.default_rng(seed + 1234) self.angles = np.linspace(-math.pi, math.pi, cfg.n_rays, endpoint=False) def sense(self, env: RoomEnvironment) -> SensorFrame: cfg = self.cfg pose = env.robot_pose # --- LiDAR / depth ------------------------------------------- ranges = env.raycast(pose, self.angles) ranges = np.clip(ranges + self.rng.normal(0.0, cfg.range_sigma, ranges.shape), 0.0, cfg.sensor_range) # --- GPS ------------------------------------------------------ gps = pose[:2] + self.rng.normal(0.0, cfg.gps_sigma, 2) # --- IMU / compass ------------------------------------------- compass = wrap_angle(pose[2] + float(self.rng.normal(0.0, cfg.compass_sigma))) gyro = float(env.robot_vel[1] + self.rng.normal(0.0, cfg.gyro_sigma)) # --- wheel encoders ------------------------------------------ enc = float(env.robot_vel[0] + self.rng.normal(0.0, cfg.encoder_sigma)) # --- object detector (people / dynamic agents) --------------- detections: Dict[int, np.ndarray] = {} for h in env.humans: d = math.hypot(h.x - pose[0], h.y - pose[1]) if d > cfg.sensor_range: continue if self.rng.random() > cfg.p_detect: continue z = np.array([h.x, h.y]) + self.rng.normal(0.0, cfg.detect_sigma, 2) detections[h.id] = z return SensorFrame( t=env.t, dt=cfg.dt, angles=self.angles, ranges=ranges, gps_xy=gps, compass_theta=compass, encoder_v=enc, gyro_w=gyro, detections=detections, ) # ===================================================================== # 4. Layer 2 -- World Construction (sensor fusion -> W_t) # ===================================================================== class TrackedObject: """Constant-velocity Kalman filter: state = [x, y, vx, vy].""" def __init__(self, oid: int, x: float, y: float, vx: float = 0.0, vy: float = 0.0): self.id = oid self.x = np.array([x, y, vx, vy], dtype=float) self.P = np.diag([0.25, 0.25, 1.00, 1.00]) self.missed = 0 def predict(self, dt: float, q: float) -> None: F = np.array([[1, 0, dt, 0], [0, 1, 0, dt], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) Q = q * np.diag([dt ** 4 / 4.0, dt ** 4 / 4.0, dt ** 2, dt ** 2]) self.x = F @ self.x self.P = F @ self.P @ F.T + Q def update(self, z: np.ndarray, R: np.ndarray) -> None: H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], dtype=float) y = z - H @ self.x S = H @ self.P @ H.T + R K = self.P @ H.T @ np.linalg.inv(S) self.x = self.x + K @ y self.P = (np.eye(4) - K @ H) @ self.P self.missed = 0 # -- convenience --------------------------------------------------- @property def position(self) -> np.ndarray: return self.x[:2].copy() @property def velocity(self) -> np.ndarray: return self.x[2:].copy() @property def pos_var(self) -> float: return float(self.P[0, 0] + self.P[1, 1]) @property def vel_var(self) -> float: return float(self.P[2, 2] + self.P[3, 3]) class WorldModel: """ Layer 2: builds the current world model W_t = { Objects, Humans, Locations, Conditions } from noisy sensor frames using a pose EKF + per-object Kalman filters. """ def __init__(self, cfg: MILKConfig): self.cfg = cfg self.pose = np.zeros(3) self.pose_cov = np.diag([1.0, 1.0, 0.5]) self.vel = np.zeros(2) self.objects: Dict[int, TrackedObject] = {} self.q_scale = 1.0 # adapted by Layer 5 self.initialised = False self.t = 0.0 # ------------------------------------------------------------------ def fuse(self, frame: SensorFrame) -> None: cfg = self.cfg dt = frame.dt if not self.initialised: self.pose = np.array([frame.gps_xy[0], frame.gps_xy[1], frame.compass_theta]) self.initialised = True else: self._predict_pose(dt, frame.encoder_v, frame.gyro_w) # predict all tracks forward to the current instant for o in self.objects.values(): o.predict(dt, cfg.track_q * self.q_scale) # measurement updates self._update_pose(frame.gps_xy, frame.compass_theta) R = np.eye(2) * (cfg.detect_sigma ** 2) for oid, z in frame.detections.items(): if oid in self.objects: self.objects[oid].update(z, R) else: self.objects[oid] = TrackedObject(oid, float(z[0]), float(z[1])) # age out stale tracks dead = [] for oid, o in self.objects.items(): if oid not in frame.detections: o.missed += 1 if o.missed > cfg.track_timeout: dead.append(oid) for oid in dead: del self.objects[oid] self.vel = np.array([frame.encoder_v, frame.gyro_w]) self.t = frame.t # ------------------------------------------------------------------ def _predict_pose(self, dt: float, v: float, w: float) -> None: x, y, th = self.pose F = np.array([[1.0, 0.0, -v * math.sin(th) * dt], [0.0, 1.0, v * math.cos(th) * dt], [0.0, 0.0, 1.0]]) self.pose = np.array([x + v * math.cos(th) * dt, y + v * math.sin(th) * dt, wrap_angle(th + w * dt)]) Q = self.q_scale * np.diag([0.010, 0.010, 0.004]) self.pose_cov = F @ self.pose_cov @ F.T + Q def _update_pose(self, z_xy: np.ndarray, z_th: float) -> None: cfg = self.cfg R = np.diag([cfg.gps_sigma ** 2, cfg.gps_sigma ** 2, cfg.compass_sigma ** 2]) y = np.array([z_xy[0] - self.pose[0], z_xy[1] - self.pose[1], wrap_angle(z_th - self.pose[2])]) S = self.pose_cov + R K = self.pose_cov @ np.linalg.inv(S) self.pose = self.pose + K @ y self.pose[2] = wrap_angle(self.pose[2]) self.pose_cov = (np.eye(3) - K) @ self.pose_cov # ------------------------------------------------------------------ def object_positions(self) -> Dict[int, np.ndarray]: return {oid: o.position for oid, o in self.objects.items()} def localisation_sigma(self) -> float: return math.sqrt(max(0.0, float(self.pose_cov[0, 0] + self.pose_cov[1, 1]))) # ===================================================================== # 5. MILK Mathematics # ===================================================================== @dataclass class MILKInfluence: """Container for the terms of the MILK dynamic equation.""" A: np.ndarray # intended action vector K: float # environmental coupling factor U: float # uncertainty score X: np.ndarray # predicted state change @property def magnitude(self) -> float: return float(np.linalg.norm(self.X)) def as_dict(self) -> Dict: return {"A": self.A.tolist(), "K": self.K, "U": self.U, "X": self.X.tolist(), "|X|": self.magnitude} def milk_dynamic_equation(A, K: float, U: float, u_min: float = 1e-3): """ The MILK dynamic equation: X_t = A_t + K_t / U_t A_t : intended action vector K_t : environmental coupling factor (how strongly the agent's action couples into the environment) U_t : uncertainty score (floored at u_min) NOTE ON NUMERICS ---------------- As U -> 0 the term K/U diverges, exactly as the source document states ("outcome estimates improve as uncertainty approaches zero"). In a physical implementation U is floored at u_min, and X is used as a *relative influence score* -- not as a literal pose delta. """ A = np.asarray(A, dtype=float) U_eff = max(float(U), float(u_min)) return A + (float(K) / U_eff) def _sse_weights(n_objects: int, cfg: MILKConfig) -> np.ndarray: base = np.array([1.0, 1.0, 0.5, # pose (x, y, theta) 0.2, 0.2, # velocity (v, omega) 1.0, 1.0]) # goal obj = np.ones(2 * n_objects) return np.concatenate([base, obj]) def state_vector(pose, vel, goal, objects: Dict[int, np.ndarray]) -> np.ndarray: """ Canonical flat state vector used for RMI / SSE computations. Object ordering is by ascending id so vectors are comparable. """ parts = [np.asarray(pose, float)[:3], np.asarray(vel, float)[:2], np.asarray(goal, float)[:2]] for k in sorted(objects): parts.append(np.asarray(objects[k], float)[:2]) return np.concatenate(parts) def reality_modification_index(s_a: np.ndarray, s_b: np.ndarray, weights: Optional[np.ndarray] = None) -> float: """ Layer metric -- Reality Modification Index: RMI = || S_future - S_current || Large values => substantial environmental change. Small values => minimal influence. """ a = np.asarray(s_a, float) b = np.asarray(s_b, float) n = min(len(a), len(b)) d = b[:n] - a[:n] if weights is not None: d = d * np.asarray(weights, float)[:n] return float(np.linalg.norm(d)) # ===================================================================== # 6. Layer 3 -- Predictive Simulation # ===================================================================== class PredictiveSimulator: """ Layer 3: generate W_{t+1} .. W_{t+n} using the world model, a constant-velocity motion model for dynamic agents, and exact differential-drive kinematics for the ego robot. (A production system would swap this for a transformer world model or a PhysX/Isaac digital twin; the interface stays identical.) """ def __init__(self, cfg: MILKConfig): self.cfg = cfg # ------------------------------------------------------------------ def rollout_robot(self, pose: np.ndarray, vel: np.ndarray, action_seq: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: """Roll the ego robot forward under a candidate action sequence.""" cfg = self.cfg traj = np.empty((len(action_seq) + 1, 3), dtype=float) vels = np.empty(len(action_seq), dtype=float) p = np.asarray(pose, float).copy() v = np.asarray(vel, float).copy() traj[0] = p for k in range(len(action_seq)): p, v = integrate_diff_drive(p, v, action_seq[k], cfg) traj[k + 1] = p vels[k] = v[0] return traj, vels # ------------------------------------------------------------------ def predict_objects(self, world: WorldModel) -> Dict[int, Tuple[np.ndarray, np.ndarray, float]]: """ Predict each tracked dynamic object over the horizon. Returns {id: (positions (H+1,2), variances (H+1,), radius)} """ cfg = self.cfg H = cfg.horizon dt = cfg.dt F = np.array([[1, 0, dt, 0], [0, 1, 0, dt], [0, 0, 1, 0], [0, 0, 0, 1]], dtype=float) Q = cfg.track_q * np.diag([dt ** 4 / 4, dt ** 4 / 4, dt ** 2, dt ** 2]) out: Dict[int, Tuple[np.ndarray, np.ndarray, float]] = {} for oid, obj in world.objects.items(): x = obj.x.copy() P = obj.P.copy() pos = np.empty((H + 1, 2)) var = np.empty(H + 1) pos[0] = x[:2] var[0] = P[0, 0] + P[1, 1] for k in range(H): x = F @ x P = F @ P @ F.T + Q pos[k + 1] = x[:2] var[k + 1] = P[0, 0] + P[1, 1] out[oid] = (pos, var, 0.30) # 0.30 m nominal agent radius return out # ------------------------------------------------------------------ def predict_next_objects(self, world: WorldModel) -> Dict[int, np.ndarray]: """One-step-ahead object prediction (used for the SSE metric).""" preds = self.predict_objects(world) return {oid: v[0][1].copy() for oid, v in preds.items()} # ===================================================================== # 7. Layer 4 -- Kinematic Optimization # ===================================================================== class KinematicOptimizer: """ Layer 4: find the action sequence minimising J = Error + Risk + Energy + Time (+ smoothness regulariser) via sampling-based receding-horizon (MPC) optimisation. """ def __init__(self, cfg: MILKConfig, sim: PredictiveSimulator, seed: int = 0): self.cfg = cfg self.sim = sim self.rng = np.random.default_rng(seed + 99) self.last_cost = float("inf") self.n_evaluated = 0 # ------------------------------------------------------------------ def _candidates(self, prev_action: np.ndarray) -> List[np.ndarray]: cfg = self.cfg H = cfg.horizon cands: List[np.ndarray] = [] vs = np.linspace(0.0, cfg.v_max, cfg.n_v_samples) ws = np.linspace(-cfg.omega_max, cfg.omega_max, cfg.n_w_samples) for v in vs: for w in ws: cands.append(np.tile([v, w], (H, 1))) # a handful of two-phase manoeuvres (turn-then-drive) half = max(1, H // 2) for _ in range(cfg.n_random): v1 = float(self.rng.uniform(0.0, cfg.v_max)) w1 = float(self.rng.uniform(-cfg.omega_max, cfg.omega_max)) v2 = float(self.rng.uniform(0.0, cfg.v_max)) w2 = float(self.rng.uniform(-cfg.omega_max, cfg.omega_max)) seq = np.vstack([np.tile([v1, w1], (half, 1)), np.tile([v2, w2], (H - half, 1))]) cands.append(seq) # always include "brake hard" cands.append(np.tile([0.0, 0.0], (H, 1))) return cands # ------------------------------------------------------------------ def _cost(self, traj: np.ndarray, vels: np.ndarray, goal: np.ndarray, pred_objs: Dict[int, Tuple[np.ndarray, np.ndarray, float]], action_seq: np.ndarray, prev_action: np.ndarray, bounds: Tuple[float, float, float, float], risk_gain: float) -> float: cfg = self.cfg dt = cfg.dt H = len(action_seq) # ---- Error : terminal distance + heading misalignment -------- final = traj[-1] d_goal = float(np.linalg.norm(final[:2] - goal)) desired = math.atan2(goal[1] - final[1], goal[0] - final[0]) head_err = abs(wrap_angle(desired - final[2])) error = d_goal + 0.25 * head_err # ---- Risk : predicted collision exposure --------------------- risk = 0.0 for oid, (pos, var, orad) in pred_objs.items(): n = min(len(pos), len(traj)) d = np.linalg.norm(pos[:n] - traj[:n, :2], axis=1) clearance = d - (cfg.robot_radius + orad) sigma = np.sqrt(var[:n]) + 0.15 risk += float(np.sum(np.exp(-np.maximum(clearance, 0.0) ** 2 / (2.0 * sigma ** 2)))) risk += 100.0 * float(np.sum(clearance < 0.0)) # ---- wall risk ------------------------------------------------ x0, x1, y0, y1 = bounds margin = cfg.robot_radius + 0.05 outside = ((traj[:, 0] < x0 + margin) | (traj[:, 0] > x1 - margin) | (traj[:, 1] < y0 + margin) | (traj[:, 1] > y1 - margin)) wall_risk = 100.0 * float(np.sum(outside)) # ---- Energy --------------------------------------------------- w_cmd = action_seq[:, 1] energy = float(np.sum(vels ** 2 + 0.30 * w_cmd ** 2) * dt) # ---- Time : expected remaining time to goal ------------------- v_avg = max(float(np.mean(np.abs(vels))), 0.20) time_term = d_goal / v_avg # ---- Smoothness ---------------------------------------------- smooth = float(np.linalg.norm(action_seq[0] - prev_action)) return (cfg.w_error * error + cfg.w_risk * risk_gain * (risk + wall_risk) + cfg.w_energy * energy + cfg.w_time * time_term + cfg.w_smooth * smooth) # ------------------------------------------------------------------ def optimize(self, pose: np.ndarray, vel: np.ndarray, goal: np.ndarray, pred_objs: Dict[int, Tuple[np.ndarray, np.ndarray, float]], prev_action: np.ndarray, bounds: Tuple[float, float, float, float], risk_gain: float = 1.0): """Returns (best_action, info_dict).""" best_seq = None best_cost = float("inf") best_traj = None best_vels = None for seq in self._candidates(prev_action): traj, vels = self.sim.rollout_robot(pose, vel, seq) c = self._cost(traj, vels, goal, pred_objs, seq, prev_action, bounds, risk_gain) if c < best_cost: best_cost = c best_seq = seq best_traj = traj best_vels = vels self.last_cost = best_cost self.n_evaluated += 1 info = { "cost": best_cost, "trajectory": best_traj, "vels": best_vels, "sequence": best_seq, } return best_seq[0].copy(), info # ===================================================================== # 8. Layer 5 -- Reality Verification # ===================================================================== class RealityVerifier: """ Layer 5: compare predicted vs. actual state and adapt the world model. Error = S_actual - S_predicted Model_new = Model_old + Learning(Error) Adaptation here adjusts the Kalman process-noise scale: persistent under-prediction of motion raises q, persistent over-prediction lowers it. """ def __init__(self, cfg: MILKConfig): self.cfg = cfg self.history: List[float] = [] self.q_scale = 1.0 self.lr = 0.08 self.target = 0.08 def verify(self, error: float) -> float: self.history.append(float(error)) return float(error) def learn(self) -> float: if not self.history: return self.q_scale e = self.history[-1] self.q_scale *= (1.0 + self.lr * (e - self.target)) self.q_scale = float(np.clip(self.q_scale, 0.25, 8.0)) return self.q_scale def mean_error(self) -> float: return float(np.mean(self.history)) if self.history else 0.0 def prediction_error(pred: Dict, gt: Dict, cfg: MILKConfig) -> Tuple[float, float, float]: """ Compute the State Synchronization Error between a prediction snapshot and ground truth. SSE = w_pose * ||pose_pred - pose_actual|| + w_obj * mean ||obj_pred - obj_actual|| Returns (sse_total, pose_error, object_error) """ p_pose = np.asarray(pred["pose"], float) a_pose = np.asarray(gt["pose"], float) e_pose = float(np.linalg.norm(p_pose[:2] - a_pose[:2])) e_objs = [] for oid, p in pred.get("objects", {}).items(): if oid in gt["objects"]: e_objs.append(float(np.linalg.norm(np.asarray(p, float)[:2] - np.asarray(gt["objects"][oid], float)[:2]))) e_obj = float(np.mean(e_objs)) if e_objs else 0.0 total = cfg.w_sse_pose * e_pose + cfg.w_sse_obj * e_obj return total, e_pose, e_obj # ===================================================================== # 9. SARAH -- Simulated Augmented Reality Assistant Human # ===================================================================== class SelfLocalizationModule: """Maintains the position estimate of the embodiment.""" def __init__(self, world: WorldModel): self.world = world @property def pose(self) -> np.ndarray: return self.world.pose @property def covariance(self) -> np.ndarray: return self.world.pose_cov def report(self) -> Dict: return { "pose": self.world.pose.tolist(), "sigma": self.world.localisation_sigma(), } class PredictiveCognitionModule: """Simulates future states of self and others.""" def __init__(self, sim: PredictiveSimulator): self.sim = sim def simulate_self(self, pose, vel, action_seq): return self.sim.rollout_robot(pose, vel, action_seq) def simulate_others(self, world: WorldModel): return self.sim.predict_objects(world) class AdaptiveLearningModule: """Updates behaviour from observed errors.""" def __init__(self, verifier: RealityVerifier): self.verifier = verifier def learn(self) -> float: return self.verifier.learn() def report(self) -> Dict: return {"q_scale": self.verifier.q_scale, "mean_sse": self.verifier.mean_error(), "n": len(self.verifier.history)} class RealitySynchronizationEngine: """ Keeps Model / Prediction / Observation mutually consistent and flags divergence. """ def __init__(self, tol: float = 0.25): self.tol = tol self.log: List[Dict] = [] def synchronize(self, model_pose, predicted_pose, observed_pose) -> Dict: mp = np.asarray(model_pose, float)[:2] pp = np.asarray(predicted_pose, float)[:2] op = np.asarray(observed_pose, float)[:2] rec = { "model_prediction": float(np.linalg.norm(mp - pp)), "prediction_observation": float(np.linalg.norm(pp - op)), "model_observation": float(np.linalg.norm(mp - op)), } rec["synchronized"] = bool(rec["prediction_observation"] < self.tol) self.log.append(rec) return rec def sync_rate(self) -> float: if not self.log: return 0.0 return float(np.mean([r["synchronized"] for r in self.log])) # ===================================================================== # 10. Controllers # ===================================================================== class BaseController: """Common perception + world-model plumbing.""" name = "BASE" def __init__(self, cfg: MILKConfig, seed: int = 0): self.cfg = cfg self.seed = seed self.perception = PerceptionLayer(cfg, seed) self.world = WorldModel(cfg) self.prev_action = np.zeros(2) self.predicted_pose = np.zeros(3) self.predicted_objects: Dict[int, np.ndarray] = {} self.last_frame: Optional[SensorFrame] = None # -- to be overridden --------------------------------------------- def act(self, goal: np.ndarray) -> np.ndarray: raise NotImplementedError def verify(self, pred: Dict, gt: Dict) -> float: return 0.0 # ------------------------------------------------------------------ def observe(self, env: RoomEnvironment) -> None: frame = self.perception.sense(env) self.last_frame = frame self.world.fuse(frame) def prediction_snapshot(self) -> Dict: return {"pose": self.predicted_pose.copy(), "objects": {k: v.copy() for k, v in self.predicted_objects.items()}} def diagnostics(self) -> Dict: return {} # --------------------------------------------------------------------- class ReactiveController(BaseController): """ Baseline: reacts to the world as it currently is. * heads straight for the goal * turns away from anything currently within a fixed radius * no rollout, no prediction of agent motion, no verification layer Its implicit prediction for the next timestep is "the world stays exactly as I currently estimate it". """ name = "REACTIVE" def __init__(self, cfg: MILKConfig, seed: int = 0): super().__init__(cfg, seed) self.avoid_radius = 1.0 def act(self, goal: np.ndarray) -> np.ndarray: cfg = self.cfg pose = self.world.pose # --- pure pursuit --------------------------------------------- desired = math.atan2(goal[1] - pose[1], goal[0] - pose[0]) err = wrap_angle(desired - pose[2]) omega = float(np.clip(2.0 * err, -cfg.omega_max, cfg.omega_max)) v = cfg.v_max * max(0.0, 1.0 - abs(err) / 1.4) # --- reflexive obstacle avoidance ----------------------------- for obj in self.world.objects.values(): rel = obj.position - pose[:2] d = float(np.linalg.norm(rel)) if d > self.avoid_radius: continue bearing = wrap_angle(math.atan2(rel[1], rel[0]) - pose[2]) if abs(bearing) < 0.8: omega = -math.copysign(cfg.omega_max * 0.85, bearing) v = min(v, 0.12) action = np.array([v, omega]) # --- the reactive "prediction": the world is frozen ------------ self.predicted_pose, _ = integrate_diff_drive(pose, self.world.vel, action, cfg) self.predicted_objects = self.world.object_positions() self.prev_action = action return action # --------------------------------------------------------------------- class MILKController(BaseController): """ Full MILK stack: Layer 1 PerceptionLayer Layer 2 WorldModel Layer 3 PredictiveSimulator Layer 4 KinematicOptimizer Layer 5 RealityVerifier + MILK dynamic equation and RMI bookkeeping """ name = "MILK" def __init__(self, cfg: MILKConfig, seed: int = 0): super().__init__(cfg, seed) self.sim = PredictiveSimulator(cfg) self.optimizer = KinematicOptimizer(cfg, self.sim, seed) self.verifier = RealityVerifier(cfg) self.uncertainty = 1.0 self.coupling = 0.0 self.influence: Optional[MILKInfluence] = None self.rmi = 0.0 self.pred_traj: Optional[np.ndarray] = None self.last_cost = float("inf") # ------------------------------------------------------------------ # Uncertainty (U_t) and environmental coupling (K_t) # ------------------------------------------------------------------ def _compute_uncertainty(self) -> float: """ U_t : scalar uncertainty over the horizon. Combines localisation variance with the propagated position uncertainty of every tracked dynamic object. """ cfg = self.cfg loc = self.world.localisation_sigma() terms = [] for o in self.world.objects.values(): growth = (cfg.horizon * cfg.dt) ** 2 * o.vel_var terms.append(o.pos_var + growth) obj = math.sqrt(float(np.mean(terms))) if terms else 0.0 return float(max(loc + 0.5 * obj, cfg.u_min)) def _compute_coupling(self) -> float: """ K_t : environmental coupling factor in [0, 1]. How strongly the agent's actions can couple into the environment: high when nearby, confidently-tracked objects are present; low in empty, featureless space. """ cfg = self.cfg objs = list(self.world.objects.values()) if not objs: return 0.15 ds = np.array([float(np.linalg.norm(o.position - self.world.pose[:2])) for o in objs]) proximity = float(np.mean(np.exp(-ds / cfg.sensor_range))) confidence = float(np.mean([math.exp(-0.5 * o.pos_var / 0.25) for o in objs])) return float(np.clip(proximity * confidence, 0.0, 1.0)) # ------------------------------------------------------------------ def act(self, goal: np.ndarray) -> np.ndarray: cfg = self.cfg pose = self.world.pose vel = self.world.vel # --- Layer 3 : simulate the future --------------------------- pred_objs = self.sim.predict_objects(self.world) # --- Layer 4 : optimise the action --------------------------- self.uncertainty = self._compute_uncertainty() self.coupling = self._compute_coupling() # Higher uncertainty -> more conservative risk weighting. risk_gain = float(np.clip(1.0 + 0.8 * (self.uncertainty - 0.15), 1.0, 3.0)) bounds = (0.0, 20.0, 0.0, 20.0) # generous; wall cost handles margins action, info = self.optimizer.optimize( pose, vel, goal, pred_objs, self.prev_action, bounds, risk_gain) self.pred_traj = info["trajectory"] self.last_cost = info["cost"] # --- MILK dynamic equation : X_t = A_t + K_t / U_t ----------- A = np.array([action[0] * cfg.dt, action[1] * cfg.dt]) X = milk_dynamic_equation(A, self.coupling, self.uncertainty, cfg.u_min) self.influence = MILKInfluence(A=A, K=self.coupling, U=self.uncertainty, X=X) # --- Reality Modification Index ------------------------------ cur_objs = self.world.object_positions() fut_objs = {oid: v[0][-1] for oid, v in pred_objs.items()} s_now = state_vector(self.world.pose, self.world.vel, goal, cur_objs) s_fut = state_vector(self.pred_traj[-1], [float(info["vels"][-1]), action[1]], goal, fut_objs) n_obj = len(set(cur_objs) | set(fut_objs)) self.rmi = reality_modification_index(s_now, s_fut, _sse_weights(n_obj, cfg)) # --- one-step predictions (for the SSE metric) --------------- self.predicted_pose = self.pred_traj[1].copy() self.predicted_objects = {oid: v[0][1].copy() for oid, v in pred_objs.items()} self.prev_action = action return action # ------------------------------------------------------------------ def verify(self, pred: Dict, gt: Dict) -> float: """Layer 5: measure error and adapt the world model.""" sse, _, _ = prediction_error(pred, gt, self.cfg) self.verifier.verify(sse) self.world.q_scale = self.verifier.learn() return sse def diagnostics(self) -> Dict: return { "U": self.uncertainty, "K": self.coupling, "X_norm": self.influence.magnitude if self.influence else 0.0, "RMI": self.rmi, "cost": self.last_cost, "q_scale": self.world.q_scale, } # --------------------------------------------------------------------- class SARAH: """ SARAH -- Simulated Augmented Reality Assistant Human. The humanoid embodiment of the MILK Protocol. Composes the four named core modules on top of the MILK control stack. """ name = "SARAH/MILK" def __init__(self, cfg: MILKConfig, seed: int = 0): self.cfg = cfg self.engine = MILKController(cfg, seed) # -- the four core modules of SARAH --------------------------- self.self_localization = SelfLocalizationModule(self.engine.world) self.predictive_cognition = PredictiveCognitionModule(self.engine.sim) self.adaptive_learning = AdaptiveLearningModule(self.engine.verifier) self.reality_sync = RealitySynchronizationEngine(tol=0.30) # -- MILK interface ------------------------------------------------ def observe(self, env: RoomEnvironment) -> None: self.engine.observe(env) def act(self, goal: np.ndarray) -> np.ndarray: return self.engine.act(goal) def prediction_snapshot(self) -> Dict: return self.engine.prediction_snapshot() def verify(self, pred: Dict, gt: Dict) -> float: sse = self.engine.verify(pred, gt) self.reality_sync.synchronize(self.engine.world.pose, pred["pose"], gt["pose"]) return sse def diagnostics(self) -> Dict: d = self.engine.diagnostics() d["sync_rate"] = self.reality_sync.sync_rate() return d # ===================================================================== # 11. Metrics # ===================================================================== @dataclass class TrialMetrics: controller: str seed: int success: bool steps: int time_to_goal: float collisions: int path_length: float energy: float mean_sse: float mean_pa: float mean_rmi: float rme: float ais: float final_goal_distance: float def as_row(self) -> str: return (f"{self.controller:<10} | {str(self.success):<5} | " f"{self.collisions:^10} | {self.time_to_goal:^7.2f} | " f"{self.path_length:^11.2f} | {self.energy:^6.2f} | " f"{self.mean_sse:^8.3f} | {self.mean_pa:^7.3f} | " f"{self.mean_rmi:^8.2f} | {self.rme:^6.3f} | {self.ais:^6.2f}") class MetricsRecorder: """Accumulates the performance metrics defined in section 11.""" def __init__(self, label: str, cfg: MILKConfig, start_goal_distance: float, seed: int): self.label = label self.cfg = cfg self.start_goal_distance = float(start_goal_distance) self.seed = seed self.sse: List[float] = [] self.rmi: List[float] = [] self.energy = 0.0 self.path_length = 0.0 self.steps = 0 self.prev_xy: Optional[np.ndarray] = None self.time_to_goal = float("nan") # ------------------------------------------------------------------ def step(self, sse: float, rmi: float, action: np.ndarray, pose_xy: np.ndarray) -> None: cfg = self.cfg self.sse.append(float(sse)) self.rmi.append(float(rmi)) # energy proxy for a differential drive: v^2 + k * omega^2 self.energy += float(action[0] ** 2 + 0.30 * action[1] ** 2) * cfg.dt if self.prev_xy is not None: self.path_length += float(np.linalg.norm(pose_xy - self.prev_xy)) self.prev_xy = np.asarray(pose_xy, float).copy() self.steps += 1 # ------------------------------------------------------------------ def finalize(self, env: RoomEnvironment, success: bool) -> TrialMetrics: cfg = self.cfg mean_sse = float(np.mean(self.sse)) if self.sse else 0.0 mean_rmi = float(np.mean(self.rmi)) if self.rmi else 0.0 # Predictive Accuracy: PA = 1 - |Predicted - Actual| (normalised) mean_pa = float(np.clip(1.0 - mean_sse / cfg.sse_scale, 0.0, 1.0)) # Reality Modification Efficiency: RME = DesiredStateChange / Energy desired_change = max(0.0, self.start_goal_distance - env.goal_distance()) energy = max(self.energy, EPS) rme = desired_change / energy # Autonomous Intelligence Score: AIS = PA * RME / SSE ais = (mean_pa * rme) / max(mean_sse, 1e-4) return TrialMetrics( controller=self.label, seed=self.seed, success=bool(success), steps=self.steps, time_to_goal=self.time_to_goal, collisions=env.collision_events, path_length=self.path_length, energy=self.energy, mean_sse=mean_sse, mean_pa=mean_pa, mean_rmi=mean_rmi, rme=rme, ais=ais, final_goal_distance=env.goal_distance(), ) # ===================================================================== # 12. Terminal renderer # ===================================================================== class ASCIIRenderer: """Minimal top-down visualisation for terminals.""" def __init__(self, env: RoomEnvironment, cols: int = 76, rows: int = 22): self.env = env self.cols = cols self.rows = rows def _cell(self, x: float, y: float) -> Tuple[int, int]: c = int(x / self.env.width * (self.cols - 1)) r = int((1.0 - y / self.env.height) * (self.rows - 1)) return (max(0, min(self.cols - 1, c)), max(0, min(self.rows - 1, r))) def render(self, controller: Optional[BaseController] = None, goal: Optional[np.ndarray] = None, header: str = "") -> str: env = self.env grid = [[" "] * self.cols for _ in range(self.rows)] for c in range(self.cols): grid[0][c] = "-" grid[self.rows - 1][c] = "-" for r in range(self.rows): grid[r][0] = "|" grid[r][self.cols - 1] = "|" # static obstacles for ob in env.static: c0, r0 = self._cell(ob.x, ob.y) grid[r0][c0] = "#" # predicted object positions (MILK only) if controller is not None: for oid, p in controller.predicted_objects.items(): c0, r0 = self._cell(float(p[0]), float(p[1])) if grid[r0][c0] == " ": grid[r0][c0] = "o" # predicted ego trajectory if getattr(controller, "pred_traj", None) is not None: for p in controller.pred_traj[1:]: c0, r0 = self._cell(float(p[0]), float(p[1])) if grid[r0][c0] == " ": grid[r0][c0] = "." # humans (ground truth) for h in env.humans: c0, r0 = self._cell(h.x, h.y) grid[r0][c0] = "H" # goal g = env.goal if goal is None else goal c0, r0 = self._cell(float(g[0]), float(g[1])) grid[r0][c0] = "G" # robot c0, r0 = self._cell(float(env.robot_pose[0]), float(env.robot_pose[1])) grid[r0][c0] = "R" lines = [header] if header else [] lines += ["".join(row) for row in grid] return "\n".join(lines) # ===================================================================== # 13. Trial runner # ===================================================================== def make_controller(kind: str, cfg: MILKConfig, seed: int): kind = kind.upper() if kind in ("REACTIVE", "BASELINE"): return ReactiveController(cfg, seed) if kind in ("MILK", "SARAH"): return SARAH(cfg, seed) raise ValueError(f"Unknown controller kind: {kind}") def run_trial(kind: str, cfg: MILKConfig, seed: int, max_steps: int = 400, render: bool = False, render_every: int = 6, verbose: bool = False) -> TrialMetrics: """Run one episode and return the resulting metrics.""" env = RoomEnvironment(cfg, seed=seed) controller = make_controller(kind, cfg, seed) start_dist = env.goal_distance() rec = MetricsRecorder(controller.name, cfg, start_dist, seed) goal = env.goal success = False renderer = ASCIIRenderer(env) if render else None for step in range(max_steps): controller.observe(env) action = controller.act(goal) # -- snapshot the prediction BEFORE the world moves ----------- pred = controller.prediction_snapshot() # -- execute --------------------------------------------------- env.step(action) # -- ground truth AFTER the step ------------------------------- gt = env.ground_truth() sse, e_pose, e_obj = prediction_error(pred, gt, cfg) # -- Layer 5 --------------------------------------------------- controller.verify(pred, gt) # -- bookkeeping ---------------------------------------------- rmi = getattr(controller, "rmi", 0.0) if not isinstance(controller, SARAH): rmi = 0.0 else: rmi = controller.engine.rmi rec.step(sse, rmi, action, env.robot_pose[:2]) if render and (step % render_every == 0): diag = controller.diagnostics() hdr = (f"[{controller.name}] step {step:03d} " f"d_goal={env.goal_distance():5.2f} " f"SSE={sse:.3f} PA={1 - min(sse / cfg.sse_scale, 1):.3f} " f"RMI={rmi:6.2f} " f"U={diag.get('U', 0):.3f} K={diag.get('K', 0):.3f}") print("\033[H\033[J" + renderer.render(controller, goal, hdr)) time.sleep(0.02) # -- termination ---------------------------------------------- if env.goal_distance() < 0.35: success = True rec.time_to_goal = (step + 1) * cfg.dt break if not success: rec.time_to_goal = float("nan") metrics = rec.finalize(env, success) if verbose: print(f" trial seed={seed} {controller.name}: " f"success={success} steps={metrics.steps} " f"SSE={metrics.mean_sse:.3f} PA={metrics.mean_pa:.3f} " f"AIS={metrics.ais:.2f}") return metrics def run_experiment(cfg: MILKConfig, n_trials: int = 3, max_steps: int = 400, verbose: bool = True) -> Dict[str, List[TrialMetrics]]: """Run Trial 1 (reactive) and Trial 2 (MILK) over matched seeds.""" results: Dict[str, List[TrialMetrics]] = {"REACTIVE": [], "SARAH/MILK": []} print("=" * 108) print("MILK PROTOCOL v1.0 -- EXPERIMENTAL VALIDATION") print("=" * 108) for seed in range(n_trials): env_seed = cfg.seed + seed print(f"\n-- Trial pair {seed + 1}/{n_trials} (env seed {env_seed}) --") m1 = run_trial("REACTIVE", cfg, env_seed, max_steps, verbose=verbose) m2 = run_trial("MILK", cfg, env_seed, max_steps, verbose=verbose) results["REACTIVE"].append(m1) results["SARAH/MILK"].append(m2) print("\n" + "=" * 108) print("RESULTS") print("=" * 108) print(f"{'CTRL':<10} | {'OK':<5} | {'COLLISIONS':^10} | {'T[s]':^7} | " f"{'PATH[m]':^11} | {'E':^6} | {'SSE':^8} | {'PA':^7} | " f"{'RMI':^8} | {'RME':^6} | {'AIS':^6}") print("-" * 108) for group in results.values(): for m in group: print(m.as_row()) print("-" * 108) print("\nSUMMARY (mean over trials)") print("-" * 108) for name, group in results.items(): print(f"{name:<12} | " f"success={np.mean([m.success for m in group]):.2f} | " f"collisions={np.mean([m.collisions for m in group]):5.2f} | " f"SSE={np.mean([m.mean_sse for m in group]):.3f} | " f"PA={np.mean([m.mean_pa for m in group]):.3f} | " f"RME={np.mean([m.rme for m in group]):.3f} | " f"AIS={np.mean([m.ais for m in group]):.2f}") # -- hypothesis test ------------------------------------------------ r_sse = np.mean([m.mean_sse for m in results["REACTIVE"]]) m_sse = np.mean([m.mean_sse for m in results["SARAH/MILK"]]) r_col = np.mean([m.collisions for m in results["REACTIVE"]]) m_col = np.mean([m.collisions for m in results["SARAH/MILK"]]) print("\nHYPOTHESIS: MILK produces lower state-transition error than a") print(" conventional reactive controller.") print(f" mean SSE reactive = {r_sse:.4f} MILK = {m_sse:.4f} " f"-> {'SUPPORTED' if m_sse < r_sse else 'NOT SUPPORTED'}") print(f" mean collisions reactive = {r_col:.2f} MILK = {m_col:.2f} " f"-> {'SUPPORTED' if m_col <= r_col else 'NOT SUPPORTED'}") print("=" * 108) return results # ===================================================================== # 14. Self-test # ===================================================================== def selftest() -> bool: """Sanity checks on the core MILK mathematics and components.""" ok = True def check(name: str, cond: bool, detail: str = "") -> None: nonlocal ok status = "PASS" if cond else "FAIL" print(f"[{status}] {name} {detail}") ok = ok and cond cfg = MILKConfig() # --- MILK dynamic equation ---------------------------------------- X = milk_dynamic_equation(np.array([0.1, 0.1]), 0.5, 0.5, cfg.u_min) check("MILK eq. basic", np.allclose(X, [1.1, 1.1]), f"X={X}") X1 = milk_dynamic_equation(np.array([0.0]), 0.5, 0.1, cfg.u_min) X2 = milk_dynamic_equation(np.array([0.0]), 0.5, 0.9, cfg.u_min) check("MILK eq. uncertainty reduces influence", float(X1[0]) > float(X2[0]), f"{X1[0]:.2f} > {X2[0]:.2f}") Xf = milk_dynamic_equation(np.array([0.0]), 0.5, 0.0, cfg.u_min) check("MILK eq. finite at U=0", np.isfinite(Xf[0]), f"X={Xf[0]:.1f}") # --- RMI ------------------------------------------------------------ a = state_vector([0, 0, 0], [0, 0], [1, 1], {0: np.array([2.0, 2.0])}) b = state_vector([0, 0, 0], [0, 0], [1, 1], {0: np.array([2.0, 2.0])}) c = state_vector([3, 4, 0], [0, 0], [1, 1], {0: np.array([2.0, 2.0])}) check("RMI identical states == 0", abs(reality_modification_index(a, b)) < 1e-9) check("RMI grows with displacement", reality_modification_index(a, c) > 4.9) # --- differential drive -------------------------------------------- pose = np.array([0.0, 0.0, 0.0]) vel = np.array([0.0, 0.0]) p1, v1 = integrate_diff_drive(pose, vel, np.array([1.0, 0.0]), cfg) check("diff-drive straight line", abs(p1[0] - 0.02) < 1e-9 and abs(p1[1]) < 1e-9, f"pose={p1}") # --- Kalman filter convergence ------------------------------------- obj = TrackedObject(0, 0.0, 0.0) rng = np.random.default_rng(0) tx, ty = 1.0, 2.0 vx, vy = 0.5, 0.2 for k in range(60): obj.predict(0.1, 0.35) tx += vx * 0.1 ty += vy * 0.1 obj.update(np.array([tx, ty]) + rng.normal(0, 0.08, 2), np.eye(2) * 0.08 ** 2) check("KF converges to true position", float(np.linalg.norm(obj.position - [tx, ty])) < 0.15, f"err={np.linalg.norm(obj.position - [tx,ty]):.4f}") check("KF estimates velocity", float(np.linalg.norm(obj.velocity - [vx, vy])) < 0.20, f"err={np.linalg.norm(obj.velocity - [vx,vy]):.4f}") # --- environment ----------------------------------------------------- env = RoomEnvironment(cfg, seed=1) readings = env.raycast(env.robot_pose, np.linspace(0, 2 * math.pi, 16, endpoint=False)) check("raycast within sensor range", bool(np.all(readings >= 0) and np.all(readings <= cfg.sensor_range + 1e-9))) # --- short smoke run -------------------------------------------------- m = run_trial("MILK", cfg, seed=0, max_steps=60, verbose=False) check("MILK produces finite metrics", math.isfinite(m.mean_sse) and math.isfinite(m.ais)) m2 = run_trial("REACTIVE", cfg, seed=0, max_steps=60, verbose=False) check("Reactive produces finite metrics", math.isfinite(m2.mean_sse) and math.isfinite(m2.ais)) print("\nSELFTEST:", "ALL PASS" if ok else "FAILURES DETECTED") return ok # ===================================================================== # 15. Entry point # ===================================================================== def main() -> int: parser = argparse.ArgumentParser( description="MILK Protocol v1.0 -- reference implementation") parser.add_argument("--trials", type=int, default=3, help="number of trial pairs (default: 3)") parser.add_argument("--seed", type=int, default=0, help="base random seed") parser.add_argument("--steps", type=int, default=400, help="max steps per trial") parser.add_argument("--demo", action="store_true", help="render a single MILK episode in the terminal") parser.add_argument("--render", action="store_true", help="render during the experiment (slow)") parser.add_argument("--controller", type=str, default="MILK", choices=["MILK", "REACTIVE"], help="controller used with --demo") parser.add_argument("--json", type=str, default=None, help="write results to a JSON file") parser.add_argument("--selftest", action="store_true", help="run internal sanity checks and exit") parser.add_argument("--horizon", type=int, default=None, help="override prediction horizon") args = parser.parse_args() cfg = MILKConfig(seed=args.seed) if args.horizon is not None: cfg.horizon = args.horizon if args.selftest: return 0 if selftest() else 1 if args.demo: print(f"Rendering a single episode with the {args.controller} " f"controller. Ctrl-C to stop.\n") run_trial(args.controller, cfg, seed=args.seed, max_steps=args.steps, render=True, render_every=4) return 0 results = run_experiment(cfg, n_trials=args.trials, max_steps=args.steps, verbose=True) if args.json: payload = { name: [asdict(m) for m in group] for name, group in results.items() } with open(args.json, "w") as fh: json.dump(payload, fh, indent=2) print(f"\nWrote {args.json}") return 0 if __name__ == "__main__": sys.exit(main())
You can encourage my continued useless #poetry, creativity and expression of self, #commentary, random thoughts, #philosophy and ideas, and by doing so your helping to feed, house and clothe a #disabled man living in #poverty, $5-10-15 It All Helps, via #cashapp at $woctxphotog or via #paypal at paypal.com/donate?campaign_id=…#TheoreticalEngineering, #TheoreticalComputing, ##TheoreticalRobotics, #TheoreticalAi
-
Gadget Review: Una Watch
https://shkspr.mobi/blog/2026/10/gadget-review-una-watch/I've never been a huge fan of smart watches. My £16 smartwatch is basically fine, but the OS is closed source and there's no way to add new functionality. Previously I had the eInk Watchy which was a pain to use and really poorly designed. Even back in 2014 I was bemoaning the design compromises in the MyKronoz ZeWatch Smart Watch.
So why did I pick up the Una Watch?
Firstly, the Una Watch is designed in Scotland from girders, and it's always nice to support local businesses.
Secondly, as a USB-C Maximalist I want gadgets which can plug in to the same cables as all my other toys. No magnetic pucks here!
Thirdly, it is (almost) completely open source.
Finally, it is repairable. You can easily unscrew it to replace the components. As my cheap smartwatch's dial has died after 12 months of use, that's a pretty compelling proposition!
Let's put it through its paces!
First Impressions
I bought mine second hand (yay for sustainability) and it arrived with a flat battery. The first charge from 0-100% took a little over an hour. My USB-C power monitor showed it taking in about 5V and 0.17 amps.
It happily charged from a PD plug, but didn't get any faster than about 0.84W. Basically, I can fully charge it on most public transport in London.
The time seemed accurate, there were options to play about with, the vibrations for notifications were easy to feel. There is an option to make it beep with every button press - I turned that off sharpish!
Disclaimer
I am not a smartwatch power user. I'm not using this to minutely track all my exercise or calculate if my heart is going to explode. I don't need cm level precision of my GPS. I didn't sync this with Strava or anything else.
Apps
The official Android app (which, sadly, isn't Open Source) worked fine on GrapheneOS. It found the watch, updated its GPS almanac, and let me browse the app store & install apps. Obviously early days, but there are a variety of community developed apps to play with.
Annoyingly the watch needs to be restarted after every app is installed - but that only take a handful of seconds.
There are some strange error messages.
That isn't the sort of message which should be shown to users.
But, on the plus side, you can install Doom!
The Screen
Oddly for a modern smartwatch, the screen stays on all the time! But this isn't some power-hungry OLED, nor is it static eInk. Instead it is a memory in pixel display - black background with orange, blue, and white pixels. The backlight remains off most of the time and is easy enough to see in daylight. It is slightly reflective - but not too bad.
Note the
??on the notifications - more on that later.Annoyingly, there's no "raise wrist to light" option. You have to interact with the watch to get the screen on. The accelerometer should allow this functionality - so perhaps it just needs to be activated in the firmware? It is bright enough to see in the dark, but not so bright it will dazzle you or people nearby.
There's no touchscreen - instead there are four buttons around the face. Up, down, select, back. I did find myself repeatedly jabbing at the screen to no avail.
So, to light it, press the back button or hold one of the other buttons.
The colour scheme is pleasant enough. I miss having a full colour display so I can see a photo of my wife whenever I glance at the screen. But the low power usage can't be argued with.
Notifications
I couldn't get notifications working at first. The app just refused to let me toggle them on. Eventually I found an app in the app-store which claimed to enable them. That didn't work either.
Unpairing, repairing, and reinstalling the app made them spring to life.
There's no notification history. Once you've clicked to read it, that's it. Gone forever. Considering this has 4GB storage, that's an odd decision.
Some of the notifications were slightly corrupt - showing question marks in place of (I assume) esoteric Unicode.
There's no way to customise the vibrate pattern - so everything "feels" the same on your wrist.
At the moment, the Una Watch sends every notification to your phone. You can't tell it to ignore certain WhatsApp groups, or only allow text messages from your spouse. The only way to get fine-grained notifications is with a third-party app like…
Gadgetbridge
You're not tied to the official app. Gadgetbridge support is excellent. There are a few things missing (you can't install apps or set alarms) - but if you want to measure your heart rate, send notifications, etc you'll be fine.
Linux Compatibility
The Una Watch plugs in to USB-C and shows up as 3.5GB of exFAT formatted storage.
You can manually edit the JSON files if you like. I think you can copy off your workout data. Or you can just use it as portable storage.
Under
lsusbit describes itself as0483:52a4 STMicroelectronics UNA WatchBattery Life
After a full day of use the battery was at around 95% - that was with a bit of GPS, several notifications, heart rate monitoring, step counting, and a bunch of fiddling. With more GPS use, that's going to be heavier on the battery.
But the joy of USB-C is that I can thwack in the same cable as I use for all my other gadgets. I can even plug it into my phone and leach a bit of power from there.
Development
The watch comes will a full Open Source SDK including lots of assets. There are several tutorials and a friendly community board.
Of course, everything has to be done in C++ - an accurs'd language which I learned in the last century and wish I'd forgotten.
Annoyingly, the TouchGFX GUI designer only works in Windows.
I'm going to try to build my own watch faces and a few niche apps.
Downsides
There are a few things this watch doesn't do - some of these may be deal-breakers for you, but weren't for me.
- No payments. There's no tap-to-pay, NFC, or anything like that.
- No microphone. You cannot speak into your Una Watch or take calls on it.
- No speaker. There's a little buzzer which can make squeaks and squawks - but you won't be playing your music through it.
- While the apps and SDK are fully open, the firmware isn't (yet).
- Can't reply to notifications.
- No maps or directions (yet).
- Step counter only shows the full day - no hour-by-hour view.
Some of these things can and will be fixed in software. Others are limitations of the hardware.
Final Thoughts
The Una Watch has dropped in price to £180. I grabbed mine 2nd hand from eBay for £120. At either price, it's decent value if you're happy to play with alpha / beta quality technology.
If you're a serious athlete, you'll probably want a more expensive and polished experience. If you are tied into the Apple or Google ecosystems, you'll probably want one of their watches.
If you like tinkering, want to experiment with new technology, or simply want to support a British company trying to build something open - then this is the watch for you. Yes, there are some rough edges, but I fundamentally believe that technology should be Open Source, repairable, and give control to its users.
#gadget #review #UnaWatch #watch -
Gadget Review: Una Watch
https://shkspr.mobi/blog/2026/10/gadget-review-una-watch/I've never been a huge fan of smart watches. My £16 smartwatch is basically fine, but the OS is closed source and there's no way to add new functionality. Previously I had the eInk Watchy which was a pain to use and really poorly designed. Even back in 2014 I was bemoaning the design compromises in the MyKronoz ZeWatch Smart Watch.
So why did I pick up the Una Watch?
Firstly, the Una Watch is designed in Scotland from girders, and it's always nice to support local businesses.
Secondly, as a USB-C Maximalist I want gadgets which can plug in to the same cables as all my other toys. No magnetic pucks here!
Thirdly, it is (almost) completely open source.
Finally, it is repairable. You can easily unscrew it to replace the components. As my cheap smartwatch's dial has died after 12 months of use, that's a pretty compelling proposition!
Let's put it through its paces!
First Impressions
I bought mine second hand (yay for sustainability) and it arrived with a flat battery. The first charge from 0-100% took a little over an hour. My USB-C power monitor showed it taking in about 5V and 0.17 amps.
It happily charged from a PD plug, but didn't get any faster than about 0.84W. Basically, I can fully charge it on most public transport in London.
The time seemed accurate, there were options to play about with, the vibrations for notifications were easy to feel. There is an option to make it beep with every button press - I turned that off sharpish!
Disclaimer
I am not a smartwatch power user. I'm not using this to minutely track all my exercise or calculate if my heart is going to explode. I don't need cm level precision of my GPS. I didn't sync this with Strava or anything else.
Apps
The official Android app (which, sadly, isn't Open Source) worked fine on GrapheneOS. It found the watch, updated its GPS almanac, and let me browse the app store & install apps. Obviously early days, but there are a variety of community developed apps to play with.
Annoyingly the watch needs to be restarted after every app is installed - but that only take a handful of seconds.
There are some strange error messages.
That isn't the sort of message which should be shown to users.
But, on the plus side, you can install Doom!
The Screen
Oddly for a modern smartwatch, the screen stays on all the time! But this isn't some power-hungry OLED, nor is it static eInk. Instead it is a memory in pixel display - black background with orange, blue, and white pixels. The backlight remains off most of the time and is easy enough to see in daylight. It is slightly reflective - but not too bad.
Note the
??on the notifications - more on that later.Annoyingly, there's no "raise wrist to light" option. You have to interact with the watch to get the screen on. The accelerometer should allow this functionality - so perhaps it just needs to be activated in the firmware? It is bright enough to see in the dark, but not so bright it will dazzle you or people nearby.
There's no touchscreen - instead there are four buttons around the face. Up, down, select, back. I did find myself repeatedly jabbing at the screen to no avail.
So, to light it, press the back button or hold one of the other buttons.
The colour scheme is pleasant enough. I miss having a full colour display so I can see a photo of my wife whenever I glance at the screen. But the low power usage can't be argued with.
Notifications
I couldn't get notifications working at first. The app just refused to let me toggle them on. Eventually I found an app in the app-store which claimed to enable them. That didn't work either.
Unpairing, repairing, and reinstalling the app made them spring to life.
There's no notification history. Once you've clicked to read it, that's it. Gone forever. Considering this has 4GB storage, that's an odd decision.
Some of the notifications were slightly corrupt - showing question marks in place of (I assume) esoteric Unicode.
There's no way to customise the vibrate pattern - so everything "feels" the same on your wrist.
At the moment, the Una Watch sends every notification to your phone. You can't tell it to ignore certain WhatsApp groups, or only allow text messages from your spouse. The only way to get fine-grained notifications is with a third-party app like…
Gadgetbridge
You're not tied to the official app. Gadgetbridge support is excellent. There are a few things missing (you can't install apps or set alarms) - but if you want to measure your heart rate, send notifications, etc you'll be fine.
Linux Compatibility
The Una Watch plugs in to USB-C and shows up as 3.5GB of exFAT formatted storage.
You can manually edit the JSON files if you like. I think you can copy off your workout data. Or you can just use it as portable storage.
Under
lsusbit describes itself as0483:52a4 STMicroelectronics UNA WatchBattery Life
After a full day of use the battery was at around 95% - that was with a bit of GPS, several notifications, heart rate monitoring, step counting, and a bunch of fiddling. With more GPS use, that's going to be heavier on the battery.
But the joy of USB-C is that I can thwack in the same cable as I use for all my other gadgets. I can even plug it into my phone and leach a bit of power from there.
Development
The watch comes will a full Open Source SDK including lots of assets. There are several tutorials and a friendly community board.
Of course, everything has to be done in C++ - an accurs'd language which I learned in the last century and wish I'd forgotten.
Annoyingly, the TouchGFX GUI designer only works in Windows.
I'm going to try to build my own watch faces and a few niche apps.
Downsides
There are a few things this watch doesn't do - some of these may be deal-breakers for you, but weren't for me.
- No payments. There's no tap-to-pay, NFC, or anything like that.
- No microphone. You cannot speak into your Una Watch or take calls on it.
- No speaker. There's a little buzzer which can make squeaks and squawks - but you won't be playing your music through it.
- While the apps and SDK are fully open, the firmware isn't (yet).
- Can't reply to notifications.
- No maps or directions (yet).
- Step counter only shows the full day - no hour-by-hour view.
Some of these things can and will be fixed in software. Others are limitations of the hardware.
Final Thoughts
The Una Watch has dropped in price to £180. I grabbed mine 2nd hand from eBay for £120. At either price, it's decent value if you're happy to play with alpha / beta quality technology.
If you're a serious athlete, you'll probably want a more expensive and polished experience. If you are tied into the Apple or Google ecosystems, you'll probably want one of their watches.
If you like tinkering, want to experiment with new technology, or simply want to support a British company trying to build something open - then this is the watch for you. Yes, there are some rough edges, but I fundamentally believe that technology should be Open Source, repairable, and give control to its users.
#gadget #review #UnaWatch #watch -
Gadget Review: Una Watch
https://shkspr.mobi/blog/2026/10/gadget-review-una-watch/I've never been a huge fan of smart watches. My £16 smartwatch is basically fine, but the OS is closed source and there's no way to add new functionality. Previously I had the eInk Watchy which was a pain to use and really poorly designed. Even back in 2014 I was bemoaning the design compromises in the MyKronoz ZeWatch Smart Watch.
So why did I pick up the Una Watch?
Firstly, the Una Watch is designed in Scotland from girders, and it's always nice to support local businesses.
Secondly, as a USB-C Maximalist I want gadgets which can plug in to the same cables as all my other toys. No magnetic pucks here!
Thirdly, it is (almost) completely open source.
Finally, it is repairable. You can easily unscrew it to replace the components. As my cheap smartwatch's dial has died after 12 months of use, that's a pretty compelling proposition!
Let's put it through its paces!
First Impressions
I bought mine second hand (yay for sustainability) and it arrived with a flat battery. The first charge from 0-100% took a little over an hour. My USB-C power monitor showed it taking in about 5V and 0.17 amps.
It happily charged from a PD plug, but didn't get any faster than about 0.84W. Basically, I can fully charge it on most public transport in London.
The time seemed accurate, there were options to play about with, the vibrations for notifications were easy to feel. There is an option to make it beep with every button press - I turned that off sharpish!
Disclaimer
I am not a smartwatch power user. I'm not using this to minutely track all my exercise or calculate if my heart is going to explode. I don't need cm level precision of my GPS. I didn't sync this with Strava or anything else.
Apps
The official Android app (which, sadly, isn't Open Source) worked fine on GrapheneOS. It found the watch, updated its GPS almanac, and let me browse the app store & install apps. Obviously early days, but there are a variety of community developed apps to play with.
Annoyingly the watch needs to be restarted after every app is installed - but that only take a handful of seconds.
There are some strange error messages.
That isn't the sort of message which should be shown to users.
But, on the plus side, you can install Doom!
The Screen
Oddly for a modern smartwatch, the screen stays on all the time! But this isn't some power-hungry OLED, nor is it static eInk. Instead it is a memory in pixel display - black background with orange, blue, and white pixels. The backlight remains off most of the time and is easy enough to see in daylight. It is slightly reflective - but not too bad.
Note the
??on the notifications - more on that later.Annoyingly, there's no "raise wrist to light" option. You have to interact with the watch to get the screen on. The accelerometer should allow this functionality - so perhaps it just needs to be activated in the firmware? It is bright enough to see in the dark, but not so bright it will dazzle you or people nearby.
There's no touchscreen - instead there are four buttons around the face. Up, down, select, back. I did find myself repeatedly jabbing at the screen to no avail.
So, to light it, press the back button or hold one of the other buttons.
The colour scheme is pleasant enough. I miss having a full colour display so I can see a photo of my wife whenever I glance at the screen. But the low power usage can't be argued with.
Notifications
I couldn't get notifications working at first. The app just refused to let me toggle them on. Eventually I found an app in the app-store which claimed to enable them. That didn't work either.
Unpairing, repairing, and reinstalling the app made them spring to life.
There's no notification history. Once you've clicked to read it, that's it. Gone forever. Considering this has 4GB storage, that's an odd decision.
Some of the notifications were slightly corrupt - showing question marks in place of (I assume) esoteric Unicode.
There's no way to customise the vibrate pattern - so everything "feels" the same on your wrist.
At the moment, the Una Watch sends every notification to your phone. You can't tell it to ignore certain WhatsApp groups, or only allow text messages from your spouse. The only way to get fine-grained notifications is with a third-party app like…
Gadgetbridge
You're not tied to the official app. Gadgetbridge support is excellent. There are a few things missing (you can't install apps or set alarms) - but if you want to measure your heart rate, send notifications, etc you'll be fine.
Linux Compatibility
The Una Watch plugs in to USB-C and shows up as 3.5GB of exFAT formatted storage.
You can manually edit the JSON files if you like. I think you can copy off your workout data. Or you can just use it as portable storage.
Under
lsusbit describes itself as0483:52a4 STMicroelectronics UNA WatchBattery Life
After a full day of use the battery was at around 95% - that was with a bit of GPS, several notifications, heart rate monitoring, step counting, and a bunch of fiddling. With more GPS use, that's going to be heavier on the battery.
But the joy of USB-C is that I can thwack in the same cable as I use for all my other gadgets. I can even plug it into my phone and leach a bit of power from there.
Development
The watch comes will a full Open Source SDK including lots of assets. There are several tutorials and a friendly community board.
Of course, everything has to be done in C++ - an accurs'd language which I learned in the last century and wish I'd forgotten.
Annoyingly, the TouchGFX GUI designer only works in Windows.
I'm going to try to build my own watch faces and a few niche apps.
Downsides
There are a few things this watch doesn't do - some of these may be deal-breakers for you, but weren't for me.
- No payments. There's no tap-to-pay, NFC, or anything like that.
- No microphone. You cannot speak into your Una Watch or take calls on it.
- No speaker. There's a little buzzer which can make squeaks and squawks - but you won't be playing your music through it.
- While the apps and SDK are fully open, the firmware isn't (yet).
- Can't reply to notifications.
- No maps or directions (yet).
- Step counter only shows the full day - no hour-by-hour view.
Some of these things can and will be fixed in software. Others are limitations of the hardware.
Final Thoughts
The Una Watch has dropped in price to £180. I grabbed mine 2nd hand from eBay for £120. At either price, it's decent value if you're happy to play with alpha / beta quality technology.
If you're a serious athlete, you'll probably want a more expensive and polished experience. If you are tied into the Apple or Google ecosystems, you'll probably want one of their watches.
If you like tinkering, want to experiment with new technology, or simply want to support a British company trying to build something open - then this is the watch for you. Yes, there are some rough edges, but I fundamentally believe that technology should be Open Source, repairable, and give control to its users.
#gadget #review #UnaWatch #watch -
Gadget Review: Una Watch
https://shkspr.mobi/blog/2026/10/gadget-review-una-watch/I've never been a huge fan of smart watches. My £16 smartwatch is basically fine, but the OS is closed source and there's no way to add new functionality. Previously I had the eInk Watchy which was a pain to use and really poorly designed. Even back in 2014 I was bemoaning the design compromises in the MyKronoz ZeWatch Smart Watch.
So why did I pick up the Una Watch?
Firstly, the Una Watch is designed in Scotland from girders, and it's always nice to support local businesses.
Secondly, as a USB-C Maximalist I want gadgets which can plug in to the same cables as all my other toys. No magnetic pucks here!
Thirdly, it is (almost) completely open source.
Finally, it is repairable. You can easily unscrew it to replace the components. As my cheap smartwatch's dial has died after 12 months of use, that's a pretty compelling proposition!
Let's put it through its paces!
First Impressions
I bought mine second hand (yay for sustainability) and it arrived with a flat battery. The first charge from 0-100% took a little over an hour. My USB-C power monitor showed it taking in about 5V and 0.17 amps.
It happily charged from a PD plug, but didn't get any faster than about 0.84W. Basically, I can fully charge it on most public transport in London.
The time seemed accurate, there were options to play about with, the vibrations for notifications were easy to feel. There is an option to make it beep with every button press - I turned that off sharpish!
Disclaimer
I am not a smartwatch power user. I'm not using this to minutely track all my exercise or calculate if my heart is going to explode. I don't need cm level precision of my GPS. I didn't sync this with Strava or anything else.
Apps
The official Android app (which, sadly, isn't Open Source) worked fine on GrapheneOS. It found the watch, updated its GPS almanac, and let me browse the app store & install apps. Obviously early days, but there are a variety of community developed apps to play with.
Annoyingly the watch needs to be restarted after every app is installed - but that only take a handful of seconds.
There are some strange error messages.
That isn't the sort of message which should be shown to users.
But, on the plus side, you can install Doom!
The Screen
Oddly for a modern smartwatch, the screen stays on all the time! But this isn't some power-hungry OLED, nor is it static eInk. Instead it is a memory in pixel display - black background with orange, blue, and white pixels. The backlight remains off most of the time and is easy enough to see in daylight. It is slightly reflective - but not too bad.
Note the
??on the notifications - more on that later.Annoyingly, there's no "raise wrist to light" option. You have to interact with the watch to get the screen on. The accelerometer should allow this functionality - so perhaps it just needs to be activated in the firmware? It is bright enough to see in the dark, but not so bright it will dazzle you or people nearby.
There's no touchscreen - instead there are four buttons around the face. Up, down, select, back. I did find myself repeatedly jabbing at the screen to no avail.
So, to light it, press the back button or hold one of the other buttons.
The colour scheme is pleasant enough. I miss having a full colour display so I can see a photo of my wife whenever I glance at the screen. But the low power usage can't be argued with.
Notifications
I couldn't get notifications working at first. The app just refused to let me toggle them on. Eventually I found an app in the app-store which claimed to enable them. That didn't work either.
Unpairing, repairing, and reinstalling the app made them spring to life.
There's no notification history. Once you've clicked to read it, that's it. Gone forever. Considering this has 4GB storage, that's an odd decision.
Some of the notifications were slightly corrupt - showing question marks in place of (I assume) esoteric Unicode.
There's no way to customise the vibrate pattern - so everything "feels" the same on your wrist.
At the moment, the Una Watch sends every notification to your phone. You can't tell it to ignore certain WhatsApp groups, or only allow text messages from your spouse. The only way to get fine-grained notifications is with a third-party app like…
Gadgetbridge
You're not tied to the official app. Gadgetbridge support is excellent. There are a few things missing (you can't install apps or set alarms) - but if you want to measure your heart rate, send notifications, etc you'll be fine.
Linux Compatibility
The Una Watch plugs in to USB-C and shows up as 3.5GB of exFAT formatted storage.
You can manually edit the JSON files if you like. I think you can copy off your workout data. Or you can just use it as portable storage.
Under
lsusbit describes itself as0483:52a4 STMicroelectronics UNA WatchBattery Life
After a full day of use the battery was at around 95% - that was with a bit of GPS, several notifications, heart rate monitoring, step counting, and a bunch of fiddling. With more GPS use, that's going to be heavier on the battery.
But the joy of USB-C is that I can thwack in the same cable as I use for all my other gadgets. I can even plug it into my phone and leach a bit of power from there.
Development
The watch comes will a full Open Source SDK including lots of assets. There are several tutorials and a friendly community board.
Of course, everything has to be done in C++ - an accurs'd language which I learned in the last century and wish I'd forgotten.
Annoyingly, the TouchGFX GUI designer only works in Windows.
I'm going to try to build my own watch faces and a few niche apps.
Downsides
There are a few things this watch doesn't do - some of these may be deal-breakers for you, but weren't for me.
- No payments. There's no tap-to-pay, NFC, or anything like that.
- No microphone. You cannot speak into your Una Watch or take calls on it.
- No speaker. There's a little buzzer which can make squeaks and squawks - but you won't be playing your music through it.
- While the apps and SDK are fully open, the firmware isn't (yet).
- Can't reply to notifications.
- No maps or directions (yet).
- Step counter only shows the full day - no hour-by-hour view.
Some of these things can and will be fixed in software. Others are limitations of the hardware.
Final Thoughts
The Una Watch has dropped in price to £180. I grabbed mine 2nd hand from eBay for £120. At either price, it's decent value if you're happy to play with alpha / beta quality technology.
If you're a serious athlete, you'll probably want a more expensive and polished experience. If you are tied into the Apple or Google ecosystems, you'll probably want one of their watches.
If you like tinkering, want to experiment with new technology, or simply want to support a British company trying to build something open - then this is the watch for you. Yes, there are some rough edges, but I fundamentally believe that technology should be Open Source, repairable, and give control to its users.
#gadget #review #UnaWatch #watch -
In July 2026 Python core developers and special guests sat down at the #Python Language Summit at #EuroPython 2026, to discuss the future of the Python programming language including free-threading, #Rust, garbage collection, and namespaces. Find a summary on the PSF blog ⬇️
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In July 2026 Python core developers and special guests sat down at the #Python Language Summit at #EuroPython 2026, to discuss the future of the Python programming language including free-threading, #Rust, garbage collection, and namespaces. Find a summary on the PSF blog ⬇️
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In July 2026 Python core developers and special guests sat down at the #Python Language Summit at #EuroPython 2026, to discuss the future of the Python programming language including free-threading, #Rust, garbage collection, and namespaces. Find a summary on the PSF blog ⬇️
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In July 2026 Python core developers and special guests sat down at the #Python Language Summit at #EuroPython 2026, to discuss the future of the Python programming language including free-threading, #Rust, garbage collection, and namespaces. Find a summary on the PSF blog ⬇️
https://pyfound.blogspot.com/2026/09/python-language-summit-2026.html -
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Scientists find an “earthquake gate” as California fault stress hits a 1,000-year high https://www.byteseu.com/2416216/ #Earthquakes;NaturalDisasters;Weather;GlobalWarming;SevereWeather;Geology;Climate;Floods #Science
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Physicists Find a Superconductor That ‘Breaks’ The Symmetry of Time – A First For Its Kind : ScienceAlert
Under certain conditions – such as bone-brittling temperatures lower than that of deep space – superconductors can conduct…
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