#aiplanning — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aiplanning, aggregated by home.social.
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OmniEVA: Bridging the 2D–3D Gap in Embodied AI
New paper introduces OmniEVA, a versatile embodied planner that pushes the boundaries of multimodal large language models (MLLMs) for robotics and spatial reasoning.
Results: OmniEVA achieves state-of-the-art performance across 2D/3D reasoning benchmarks and outperforms existing models in object navigation tasks.
Paper: https://arxiv.org/pdf/2509.09332v1
Project: https://omnieva.github.io/#EmbodiedAI #Robotics #LLM #MLLM #3DVision #AIResearch #AIPlanning
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OmniEVA: Bridging the 2D–3D Gap in Embodied AI
New paper introduces OmniEVA, a versatile embodied planner that pushes the boundaries of multimodal large language models (MLLMs) for robotics and spatial reasoning.
Results: OmniEVA achieves state-of-the-art performance across 2D/3D reasoning benchmarks and outperforms existing models in object navigation tasks.
Paper: https://arxiv.org/pdf/2509.09332v1
Project: https://omnieva.github.io/#EmbodiedAI #Robotics #LLM #MLLM #3DVision #AIResearch #AIPlanning
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OmniEVA: Bridging the 2D–3D Gap in Embodied AI
New paper introduces OmniEVA, a versatile embodied planner that pushes the boundaries of multimodal large language models (MLLMs) for robotics and spatial reasoning.
Results: OmniEVA achieves state-of-the-art performance across 2D/3D reasoning benchmarks and outperforms existing models in object navigation tasks.
Paper: https://arxiv.org/pdf/2509.09332v1
Project: https://omnieva.github.io/#EmbodiedAI #Robotics #LLM #MLLM #3DVision #AIResearch #AIPlanning
-
OmniEVA: Bridging the 2D–3D Gap in Embodied AI
New paper introduces OmniEVA, a versatile embodied planner that pushes the boundaries of multimodal large language models (MLLMs) for robotics and spatial reasoning.
Results: OmniEVA achieves state-of-the-art performance across 2D/3D reasoning benchmarks and outperforms existing models in object navigation tasks.
Paper: https://arxiv.org/pdf/2509.09332v1
Project: https://omnieva.github.io/#EmbodiedAI #Robotics #LLM #MLLM #3DVision #AIResearch #AIPlanning
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OmniEVA: Bridging the 2D–3D Gap in Embodied AI
New paper introduces OmniEVA, a versatile embodied planner that pushes the boundaries of multimodal large language models (MLLMs) for robotics and spatial reasoning.
Results: OmniEVA achieves state-of-the-art performance across 2D/3D reasoning benchmarks and outperforms existing models in object navigation tasks.
Paper: https://arxiv.org/pdf/2509.09332v1
Project: https://omnieva.github.io/#EmbodiedAI #Robotics #LLM #MLLM #3DVision #AIResearch #AIPlanning
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OpenAI confirms GPT-6 is in fast track development. SMBs must align budgets & upskill teams now for seamless AI adoption. #AIPlanning #SMBTech
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If you need efficient constraint solving with long-term support, Timefold is the future. Less waiting, more optimizing. 🚀 #Optimization #Timefold #AIPlanning
Having used OptaPlanner in multiple projects and now testing Timefold, I can confidently say it outperforms OptaPlanner in every way. The devs are active and receptive while Red Hat seems to have quietly shelved OptaPlanner moving it to the KIE / Apache team who do not offer support.
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If you need efficient constraint solving with long-term support, Timefold is the future. Less waiting, more optimizing. 🚀 #Optimization #Timefold #AIPlanning
Having used OptaPlanner in multiple projects and now testing Timefold, I can confidently say it outperforms OptaPlanner in every way. The devs are active and receptive while Red Hat seems to have quietly shelved OptaPlanner moving it to the KIE / Apache team who do not offer support.
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If you need efficient constraint solving with long-term support, Timefold is the future. Less waiting, more optimizing. 🚀 #Optimization #Timefold #AIPlanning
Having used OptaPlanner in multiple projects and now testing Timefold, I can confidently say it outperforms OptaPlanner in every way. The devs are active and receptive while Red Hat seems to have quietly shelved OptaPlanner moving it to the KIE / Apache team who do not offer support.
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ICYMI: Crafting an AI Strategy Roadmap: Long-Term Planning for Sustainable Growth https://kamyarshah.com/crafting-an-ai-strategy-roadmap-long-term-planning-for-sustainable-growth/ #Blog #Infographics #AIadaptation #AIimplementation #AIplanning
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Crafting an AI Strategy Roadmap: Long-Term Planning for Sustainable Growth https://kamyarshah.com/crafting-an-ai-strategy-roadmap-long-term-planning-for-sustainable-growth/ #Blog #Infographics #AIadaptation #AIimplementation #AIplanning
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RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models: https://arxiv.org/pdf/2409.12294
We saw how #LLMs and #planners #aiplanning help each to produce behavior. It also works with data: coupling #GenAI with an actual #database may yield better results. This is called #RAG.
This paper shows an application of the state-of-the-art of using RAG for interactive #robotics, by proposing a framework. It is not mature, but it lays down the architecture to get started.
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RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models: https://arxiv.org/pdf/2409.12294
We saw how #LLMs and #planners #aiplanning help each to produce behavior. It also works with data: coupling #GenAI with an actual #database may yield better results. This is called #RAG.
This paper shows an application of the state-of-the-art of using RAG for interactive #robotics, by proposing a framework. It is not mature, but it lays down the architecture to get started.
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RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models: https://arxiv.org/pdf/2409.12294
We saw how #LLMs and #planners #aiplanning help each to produce behavior. It also works with data: coupling #GenAI with an actual #database may yield better results. This is called #RAG.
This paper shows an application of the state-of-the-art of using RAG for interactive #robotics, by proposing a framework. It is not mature, but it lays down the architecture to get started.
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RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models: https://arxiv.org/pdf/2409.12294
We saw how #LLMs and #planners #aiplanning help each to produce behavior. It also works with data: coupling #GenAI with an actual #database may yield better results. This is called #RAG.
This paper shows an application of the state-of-the-art of using RAG for interactive #robotics, by proposing a framework. It is not mature, but it lays down the architecture to get started.
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Meet Majid Khadiv, our newly appointed Professor of #AIPlanning in #DynamicEnvironments, in the latest "NewIn" episode. His research focuses on how to enable #robots to perform tasks that are dangerous for humans, such as putting out fires: http://go.tum.de/413334 🤖
📷 A.Heddergott
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Meet Majid Khadiv, our newly appointed Professor of #AIPlanning in #DynamicEnvironments, in the latest "NewIn" episode. His research focuses on how to enable #robots to perform tasks that are dangerous for humans, such as putting out fires: http://go.tum.de/413334 🤖
📷 A.Heddergott
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Meet Majid Khadiv, our newly appointed Professor of #AIPlanning in #DynamicEnvironments, in the latest "NewIn" episode. His research focuses on how to enable #robots to perform tasks that are dangerous for humans, such as putting out fires: http://go.tum.de/413334 🤖
📷 A.Heddergott
-
Meet Majid Khadiv, our newly appointed Professor of #AIPlanning in #DynamicEnvironments, in the latest "NewIn" episode. His research focuses on how to enable #robots to perform tasks that are dangerous for humans, such as putting out fires: http://go.tum.de/413334 🤖
📷 A.Heddergott
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Meet Majid Khadiv, our newly appointed Professor of #AIPlanning in #DynamicEnvironments, in the latest "NewIn" episode. His research focuses on how to enable #robots to perform tasks that are dangerous for humans, such as putting out fires: http://go.tum.de/413334 🤖
📷 A.Heddergott
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@billjanssen Agreed.
Unfortunately for me (and us at #PARC) we love to be in the scientific cracks and do inter-disciplinary research. So we are constantly fighting this war of oh this is not #HCI, not #AI enough, this sorta looks like #AIPlanning but not, this is more transportation than #AI.
Thankfully, the journals are more creative.
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@billjanssen Agreed.
Unfortunately for me (and us at #PARC) we love to be in the scientific cracks and do inter-disciplinary research. So we are constantly fighting this war of oh this is not #HCI, not #AI enough, this sorta looks like #AIPlanning but not, this is more transportation than #AI.
Thankfully, the journals are more creative.
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@billjanssen Agreed.
Unfortunately for me (and us at #PARC) we love to be in the scientific cracks and do inter-disciplinary research. So we are constantly fighting this war of oh this is not #HCI, not #AI enough, this sorta looks like #AIPlanning but not, this is more transportation than #AI.
Thankfully, the journals are more creative.
-
@billjanssen Agreed.
Unfortunately for me (and us at #PARC) we love to be in the scientific cracks and do inter-disciplinary research. So we are constantly fighting this war of oh this is not #HCI, not #AI enough, this sorta looks like #AIPlanning but not, this is more transportation than #AI.
Thankfully, the journals are more creative.
-
@billjanssen Agreed.
Unfortunately for me (and us at #PARC) we love to be in the scientific cracks and do inter-disciplinary research. So we are constantly fighting this war of oh this is not #HCI, not #AI enough, this sorta looks like #AIPlanning but not, this is more transportation than #AI.
Thankfully, the journals are more creative.
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On the other hand, some #LLM papers claim #AIPlanning while not really solving the "learning how to plan" problem. These papers were published at exclusively #NLP venues and had no (apparent) input from #AIPlanning or #Agents communities.
Frustratingly, #AI publishing is operating with this rule:
if #ML #DL -> can do magical things.
If not #ML #DL -> why aren't you doing #ML #DL or what about this other #ML method claiming to solve this other problem. -
On the other hand, some #LLM papers claim #AIPlanning while not really solving the "learning how to plan" problem. These papers were published at exclusively #NLP venues and had no (apparent) input from #AIPlanning or #Agents communities.
Frustratingly, #AI publishing is operating with this rule:
if #ML #DL -> can do magical things.
If not #ML #DL -> why aren't you doing #ML #DL or what about this other #ML method claiming to solve this other problem. -
On the other hand, some #LLM papers claim #AIPlanning while not really solving the "learning how to plan" problem. These papers were published at exclusively #NLP venues and had no (apparent) input from #AIPlanning or #Agents communities.
Frustratingly, #AI publishing is operating with this rule:
if #ML #DL -> can do magical things.
If not #ML #DL -> why aren't you doing #ML #DL or what about this other #ML method claiming to solve this other problem. -
On the other hand, some #LLM papers claim #AIPlanning while not really solving the "learning how to plan" problem. These papers were published at exclusively #NLP venues and had no (apparent) input from #AIPlanning or #Agents communities.
Frustratingly, #AI publishing is operating with this rule:
if #ML #DL -> can do magical things.
If not #ML #DL -> why aren't you doing #ML #DL or what about this other #ML method claiming to solve this other problem. -
On the other hand, some #LLM papers claim #AIPlanning while not really solving the "learning how to plan" problem. These papers were published at exclusively #NLP venues and had no (apparent) input from #AIPlanning or #Agents communities.
Frustratingly, #AI publishing is operating with this rule:
if #ML #DL -> can do magical things.
If not #ML #DL -> why aren't you doing #ML #DL or what about this other #ML method claiming to solve this other problem. -
#AI #ML research/publishing operates in silos - to the detriment of making progress.
Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.
The bad reason: "this is not just planning but also something similar to reinforcement learning".
Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.
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#AI #ML research/publishing operates in silos - to the detriment of making progress.
Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.
The bad reason: "this is not just planning but also something similar to reinforcement learning".
Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.
-
#AI #ML research/publishing operates in silos - to the detriment of making progress.
Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.
The bad reason: "this is not just planning but also something similar to reinforcement learning".
Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.
-
#AI #ML research/publishing operates in silos - to the detriment of making progress.
Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.
The bad reason: "this is not just planning but also something similar to reinforcement learning".
Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.
-
#AI #ML research/publishing operates in silos - to the detriment of making progress.
Our #IJCAI submission on #OpenWorldLearning #OWL was rejected for good and bad reasons.
The bad reason: "this is not just planning but also something similar to reinforcement learning".
Guess what - that is the point of our research! We are trying to close the gap between designed #AIPlanning systems and adaptive #Learning systems. It is a super-hard gap to push #AI #ML algorithmic research in.
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Common wisdom in #AI and #ML is that #AIPlanning methods cannot deal with continuous state and action spaces.
Subverting these expectations - presenting our recent paper at #ICAPS23 on how a planning agent can play #AngryBirds!
And, no #DL #DQN systems cannot play these games yet AND take so much data to learn to play a single level.
#Planning #Reasoning #KRR FTW!!
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Common wisdom in #AI and #ML is that #AIPlanning methods cannot deal with continuous state and action spaces.
Subverting these expectations - presenting our recent paper at #ICAPS23 on how a planning agent can play #AngryBirds!
And, no #DL #DQN systems cannot play these games yet AND take so much data to learn to play a single level.
#Planning #Reasoning #KRR FTW!!
-
Common wisdom in #AI and #ML is that #AIPlanning methods cannot deal with continuous state and action spaces.
Subverting these expectations - presenting our recent paper at #ICAPS23 on how a planning agent can play #AngryBirds!
And, no #DL #DQN systems cannot play these games yet AND take so much data to learn to play a single level.
#Planning #Reasoning #KRR FTW!!
-
Common wisdom in #AI and #ML is that #AIPlanning methods cannot deal with continuous state and action spaces.
Subverting these expectations - presenting our recent paper at #ICAPS23 on how a planning agent can play #AngryBirds!
And, no #DL #DQN systems cannot play these games yet AND take so much data to learn to play a single level.
#Planning #Reasoning #KRR FTW!!
-
Common wisdom in #AI and #ML is that #AIPlanning methods cannot deal with continuous state and action spaces.
Subverting these expectations - presenting our recent paper at #ICAPS23 on how a planning agent can play #AngryBirds!
And, no #DL #DQN systems cannot play these games yet AND take so much data to learn to play a single level.
#Planning #Reasoning #KRR FTW!!
-
January has been an exciting month for #AI #ML fundamental research at #PARC.
Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.
#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program
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January has been an exciting month for #AI #ML fundamental research at #PARC.
Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.
#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program
-
January has been an exciting month for #AI #ML fundamental research at #PARC.
Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.
#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program
-
January has been an exciting month for #AI #ML fundamental research at #PARC.
Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.
#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program
-
January has been an exciting month for #AI #ML fundamental research at #PARC.
Our work on making #AIPlanning methods work/learn in an #OpenWorld -will be presented at #AAMAS2023 as well as at #ICAPS2023. AND, an #AIJ article is under works.
#OpenWorldLearning is a new challenge - the environments introduce novelties while the agent is operating in the world. The agent must detect, characterize, and accommodate novelties during run time. This research is a part of #DARPA #SAILON program