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#covariant — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #covariant, aggregated by home.social.

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  1. Whistleblower claims Amazon paid $380M for licensing robot maker Covariant's tech & hiring its staff, raising questions about potential antitrust concerns. 🚨 Could this deal be under investigation? #Amazon #Covariant #TechNews #Antitrust #AI #Robotics #BusinessNews #Innovation

  2. Whistleblower claims Amazon paid $380M for licensing robot maker Covariant's tech & hiring its staff, raising questions about potential antitrust concerns. 🚨 Could this deal be under investigation? #Amazon #Covariant #TechNews #Antitrust #AI #Robotics #BusinessNews #Innovation

  3. Whistleblower claims Amazon paid $380M for licensing robot maker Covariant's tech & hiring its staff, raising questions about potential antitrust concerns. 🚨 Could this deal be under investigation? #Amazon #Covariant #TechNews #Antitrust #AI #Robotics #BusinessNews #Innovation

  4. Whistleblower claims Amazon paid $380M for licensing robot maker Covariant's tech & hiring its staff, raising questions about potential antitrust concerns. 🚨 Could this deal be under investigation? #Amazon #Covariant #TechNews #Antitrust #AI #Robotics #BusinessNews #Innovation

  5. Whistleblower claims Amazon paid $380M for licensing robot maker Covariant's tech & hiring its staff, raising questions about potential antitrust concerns. 🚨 Could this deal be under investigation? #Amazon #Covariant #TechNews #Antitrust #AI #Robotics #BusinessNews #Innovation

  6. In one popular robot training technique, called #imitation #learning, models learn to perform tasks by, for example, imitating the actions of a human teleoperating a robot or using a VR headset to collect data on a robot.

    It’s a technique that has gone in and out of fashion over decades but has recently become more popular with robots that do manipulation tasks, says Russ Tedrake, vice president of robotics research at the Toyota Research Institute and an MIT professor.
    By pairing this technique with #generative #AI, researchers at the Toyota Research Institute, Columbia University, and MIT have been able to quickly teach robots to do many new tasks.

    They believe they have found a way to extend the technology propelling generative AI from the realm of text, images, and videos into the domain of robot movements. 
    The idea is to start with a human, who manually controls the robot to #demonstrate #behaviors such as whisking eggs or picking up plates.

    Using a technique called #diffusion #policy, the robot is then able to use the data fed into it to learn skills.
    The researchers have taught robots more than 200 skills, such as peeling vegetables and pouring liquids, and say they are working toward teaching 1,000 skills by the end of the year. 

    Many others have taken advantage of generative AI as well. #Covariant, a robotics startup that spun off from OpenAI’s now-shuttered robotics research unit, has built a multimodal model called RFM-1.
    It can accept prompts in the form of text, image, video, robot instructions, or measurements. 
    Generative AI allows the robot to both understand instructions and generate images or videos relating to those tasks. 

    The Toyota Research Institute team hopes this will one day lead to
    🔸 “large behavior models,” 🔸which are analogous to large language models, says Tedrake
    “A lot of people think behavior cloning is going to get us to a ChatGPT moment for robotics,” he says. 

    In a similar demonstration, earlier this year a team at Stanford managed to use a relatively cheap off-the-shelf robot costing $32,000 to do complex manipulation tasks such as cooking shrimp and cleaning stains. It learned those new skills quickly with AI. 

    Called Mobile ALOHA (a loose acronym for “a low-cost open-source hardware teleoperation system”), the robot learned to cook shrimp with the help of just 20 human demonstrations and data from other tasks, such as tearing off a paper towel or piece of tape.
    The Stanford researchers found that AI can help robots acquire transferable skills: training on one task can improve its performance for others

    technologyreview.com/2024/04/1

  7. In one popular robot training technique, called #imitation #learning, models learn to perform tasks by, for example, imitating the actions of a human teleoperating a robot or using a VR headset to collect data on a robot.

    It’s a technique that has gone in and out of fashion over decades but has recently become more popular with robots that do manipulation tasks, says Russ Tedrake, vice president of robotics research at the Toyota Research Institute and an MIT professor.
    By pairing this technique with #generative #AI, researchers at the Toyota Research Institute, Columbia University, and MIT have been able to quickly teach robots to do many new tasks.

    They believe they have found a way to extend the technology propelling generative AI from the realm of text, images, and videos into the domain of robot movements. 
    The idea is to start with a human, who manually controls the robot to #demonstrate #behaviors such as whisking eggs or picking up plates.

    Using a technique called #diffusion #policy, the robot is then able to use the data fed into it to learn skills.
    The researchers have taught robots more than 200 skills, such as peeling vegetables and pouring liquids, and say they are working toward teaching 1,000 skills by the end of the year. 

    Many others have taken advantage of generative AI as well. #Covariant, a robotics startup that spun off from OpenAI’s now-shuttered robotics research unit, has built a multimodal model called RFM-1.
    It can accept prompts in the form of text, image, video, robot instructions, or measurements. 
    Generative AI allows the robot to both understand instructions and generate images or videos relating to those tasks. 

    The Toyota Research Institute team hopes this will one day lead to
    🔸 “large behavior models,” 🔸which are analogous to large language models, says Tedrake
    “A lot of people think behavior cloning is going to get us to a ChatGPT moment for robotics,” he says. 

    In a similar demonstration, earlier this year a team at Stanford managed to use a relatively cheap off-the-shelf robot costing $32,000 to do complex manipulation tasks such as cooking shrimp and cleaning stains. It learned those new skills quickly with AI. 

    Called Mobile ALOHA (a loose acronym for “a low-cost open-source hardware teleoperation system”), the robot learned to cook shrimp with the help of just 20 human demonstrations and data from other tasks, such as tearing off a paper towel or piece of tape.
    The Stanford researchers found that AI can help robots acquire transferable skills: training on one task can improve its performance for others

    technologyreview.com/2024/04/1

  8. In one popular robot training technique, called #imitation #learning, models learn to perform tasks by, for example, imitating the actions of a human teleoperating a robot or using a VR headset to collect data on a robot.

    It’s a technique that has gone in and out of fashion over decades but has recently become more popular with robots that do manipulation tasks, says Russ Tedrake, vice president of robotics research at the Toyota Research Institute and an MIT professor.
    By pairing this technique with #generative #AI, researchers at the Toyota Research Institute, Columbia University, and MIT have been able to quickly teach robots to do many new tasks.

    They believe they have found a way to extend the technology propelling generative AI from the realm of text, images, and videos into the domain of robot movements. 
    The idea is to start with a human, who manually controls the robot to #demonstrate #behaviors such as whisking eggs or picking up plates.

    Using a technique called #diffusion #policy, the robot is then able to use the data fed into it to learn skills.
    The researchers have taught robots more than 200 skills, such as peeling vegetables and pouring liquids, and say they are working toward teaching 1,000 skills by the end of the year. 

    Many others have taken advantage of generative AI as well. #Covariant, a robotics startup that spun off from OpenAI’s now-shuttered robotics research unit, has built a multimodal model called RFM-1.
    It can accept prompts in the form of text, image, video, robot instructions, or measurements. 
    Generative AI allows the robot to both understand instructions and generate images or videos relating to those tasks. 

    The Toyota Research Institute team hopes this will one day lead to
    🔸 “large behavior models,” 🔸which are analogous to large language models, says Tedrake
    “A lot of people think behavior cloning is going to get us to a ChatGPT moment for robotics,” he says. 

    In a similar demonstration, earlier this year a team at Stanford managed to use a relatively cheap off-the-shelf robot costing $32,000 to do complex manipulation tasks such as cooking shrimp and cleaning stains. It learned those new skills quickly with AI. 

    Called Mobile ALOHA (a loose acronym for “a low-cost open-source hardware teleoperation system”), the robot learned to cook shrimp with the help of just 20 human demonstrations and data from other tasks, such as tearing off a paper towel or piece of tape.
    The Stanford researchers found that AI can help robots acquire transferable skills: training on one task can improve its performance for others

    technologyreview.com/2024/04/1

  9. In one popular robot training technique, called #imitation #learning, models learn to perform tasks by, for example, imitating the actions of a human teleoperating a robot or using a VR headset to collect data on a robot.

    It’s a technique that has gone in and out of fashion over decades but has recently become more popular with robots that do manipulation tasks, says Russ Tedrake, vice president of robotics research at the Toyota Research Institute and an MIT professor.
    By pairing this technique with #generative #AI, researchers at the Toyota Research Institute, Columbia University, and MIT have been able to quickly teach robots to do many new tasks.

    They believe they have found a way to extend the technology propelling generative AI from the realm of text, images, and videos into the domain of robot movements. 
    The idea is to start with a human, who manually controls the robot to #demonstrate #behaviors such as whisking eggs or picking up plates.

    Using a technique called #diffusion #policy, the robot is then able to use the data fed into it to learn skills.
    The researchers have taught robots more than 200 skills, such as peeling vegetables and pouring liquids, and say they are working toward teaching 1,000 skills by the end of the year. 

    Many others have taken advantage of generative AI as well. #Covariant, a robotics startup that spun off from OpenAI’s now-shuttered robotics research unit, has built a multimodal model called RFM-1.
    It can accept prompts in the form of text, image, video, robot instructions, or measurements. 
    Generative AI allows the robot to both understand instructions and generate images or videos relating to those tasks. 

    The Toyota Research Institute team hopes this will one day lead to
    🔸 “large behavior models,” 🔸which are analogous to large language models, says Tedrake
    “A lot of people think behavior cloning is going to get us to a ChatGPT moment for robotics,” he says. 

    In a similar demonstration, earlier this year a team at Stanford managed to use a relatively cheap off-the-shelf robot costing $32,000 to do complex manipulation tasks such as cooking shrimp and cleaning stains. It learned those new skills quickly with AI. 

    Called Mobile ALOHA (a loose acronym for “a low-cost open-source hardware teleoperation system”), the robot learned to cook shrimp with the help of just 20 human demonstrations and data from other tasks, such as tearing off a paper towel or piece of tape.
    The Stanford researchers found that AI can help robots acquire transferable skills: training on one task can improve its performance for others

    technologyreview.com/2024/04/1

  10. In one popular robot training technique, called #imitation #learning, models learn to perform tasks by, for example, imitating the actions of a human teleoperating a robot or using a VR headset to collect data on a robot.

    It’s a technique that has gone in and out of fashion over decades but has recently become more popular with robots that do manipulation tasks, says Russ Tedrake, vice president of robotics research at the Toyota Research Institute and an MIT professor.
    By pairing this technique with #generative #AI, researchers at the Toyota Research Institute, Columbia University, and MIT have been able to quickly teach robots to do many new tasks.

    They believe they have found a way to extend the technology propelling generative AI from the realm of text, images, and videos into the domain of robot movements. 
    The idea is to start with a human, who manually controls the robot to #demonstrate #behaviors such as whisking eggs or picking up plates.

    Using a technique called #diffusion #policy, the robot is then able to use the data fed into it to learn skills.
    The researchers have taught robots more than 200 skills, such as peeling vegetables and pouring liquids, and say they are working toward teaching 1,000 skills by the end of the year. 

    Many others have taken advantage of generative AI as well. #Covariant, a robotics startup that spun off from OpenAI’s now-shuttered robotics research unit, has built a multimodal model called RFM-1.
    It can accept prompts in the form of text, image, video, robot instructions, or measurements. 
    Generative AI allows the robot to both understand instructions and generate images or videos relating to those tasks. 

    The Toyota Research Institute team hopes this will one day lead to
    🔸 “large behavior models,” 🔸which are analogous to large language models, says Tedrake
    “A lot of people think behavior cloning is going to get us to a ChatGPT moment for robotics,” he says. 

    In a similar demonstration, earlier this year a team at Stanford managed to use a relatively cheap off-the-shelf robot costing $32,000 to do complex manipulation tasks such as cooking shrimp and cleaning stains. It learned those new skills quickly with AI. 

    Called Mobile ALOHA (a loose acronym for “a low-cost open-source hardware teleoperation system”), the robot learned to cook shrimp with the help of just 20 human demonstrations and data from other tasks, such as tearing off a paper towel or piece of tape.
    The Stanford researchers found that AI can help robots acquire transferable skills: training on one task can improve its performance for others

    technologyreview.com/2024/04/1

  11. CW: programming, java

    is there a name for the #ExistentialType pattern in #java to avoid wildcards (especially to reduce confusion about where the quantifiers are when returning a #HeterogenousCollection like Set<Foo<?>>)? I’m thinking along the lines of

    public sealed interface AnyFoo permits Foo {
        // Methods of Foo<T> that do not involve any T type variable
    }
    
    public non-sealed class Foo<T> implements AnyFoo {
        // Methods of Foo<T> that involve some T
    }

    I had students get very confused when the T type variable degrades to Object / Void based on the #covariant / #contravariant position, so enforcing the presence of an explicit cast might be beneficial. What is extra nice is that

    AnyFoo anyFoo;
    Foo<?> foo = (Foo<?>) anyFoo;

    is permitted without an unsafe cast warning due to the sealed keyword (although, to be honest, something like

    <T> void processFoo(Foo<T> foo);
    processFoo((Foo<?>) anyFoo);

    is much preferable – if you have a Skolem type variables lying around, the least you can do is naming them! :blobfoxscience:)

  12. CW: programming, java

    is there a name for the #ExistentialType pattern in #java to avoid wildcards (especially to reduce confusion about where the quantifiers are when returning a #HeterogenousCollection like Set<Foo<?>>)? I’m thinking along the lines of

    public sealed interface AnyFoo permits Foo {
        // Methods of Foo<T> that do not involve any T type variable
    }
    
    public non-sealed class Foo<T> implements AnyFoo {
        // Methods of Foo<T> that involve some T
    }

    I had students get very confused when the T type variable degrades to Object / Void based on the #covariant / #contravariant position, so enforcing the presence of an explicit cast might be beneficial. What is extra nice is that

    AnyFoo anyFoo;
    Foo<?> foo = (Foo<?>) anyFoo;

    is permitted without an unsafe cast warning due to the sealed keyword (although, to be honest, something like

    <T> void processFoo(Foo<T> foo);
    processFoo((Foo<?>) anyFoo);

    is much preferable – if you have a Skolem type variables lying around, the least you can do is naming them! :blobfoxscience:)

  13. CW: programming, java

    is there a name for the #ExistentialType pattern in #java to avoid wildcards (especially to reduce confusion about where the quantifiers are when returning a #HeterogenousCollection like Set<Foo<?>>)? I’m thinking along the lines of

    public sealed interface AnyFoo permits Foo {
        // Methods of Foo<T> that do not involve any T type variable
    }
    
    public non-sealed class Foo<T> implements AnyFoo {
        // Methods of Foo<T> that involve some T
    }

    I had students get very confused when the T type variable degrades to Object / Void based on the #covariant / #contravariant position, so enforcing the presence of an explicit cast might be beneficial. What is extra nice is that

    AnyFoo anyFoo;
    Foo<?> foo = (Foo<?>) anyFoo;

    is permitted without an unsafe cast warning due to the sealed keyword (although, to be honest, something like

    <T> void processFoo(Foo<T> foo);
    processFoo((Foo<?>) anyFoo);

    is much preferable – if you have a Skolem type variables lying around, the least you can do is naming them! :blobfoxscience:)

  14. They’re programmed to work hard and play hard - Industrial robotics are big and heavy — and in some cases, legitimately dangerous.... - feedproxy.google.com/~r/Techcr #roboticsroundup #covariant #intrinsic #robotics #agility #toyota

  15. Industrial AI startup Covariant raises a $40M Series B - Covariant this week announced that it has raised a $40 million Series B, led by Index Ventures. The ... more: feedproxy.google.com/~r/Techcr #recentfunding #indexventures #pieterabbeel #covariant #robotics #startups