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    The challenge of solving robotic touch

    Another post calls Astra, Fable and Muse’s zero-shot performance on robotics and world-model benchmarks a leap in progress, while saying it senses despair among academics.

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    TLDR

    One post says Astra, Fable and Muse are tackling robotics and world-model benchmarks “zero-shot”—without task-specific examples—and characterizes this as a leap in progress. It also describes sensing despair among academics. A post quoting that assessment emphasizes unfinished work: robotic touch remains unsolved, it argues, and there is little training data, leaving interesting research to do.

    Combined views

    26.4K

    8 Sources, first seen 18d ago

    Combined views

    26.4K

    8 Sources, first seen 18d ago

    284 likes
    18d ago
    first seen 18d ago
    284 likes
    16 comments
    55 saves
    43 reposts
    16 comments
    55 saves
    43 reposts

    Sentiment

    Positive31.8%68.2%Negative

    Summary

    Sentiment

    Positive31.8%68.2%Negative

    Replies voiced despair over new models like Astra and Fable zero-shotting robotics benchmarks, citing doubts about overfitting and limited novelty, while some accounts saw opportunities in applications or real step changes on private tests.

    Based on 24 sentiment-bearing replies from 22 accounts across 3 conversations.

    Summary

    Replies voiced despair over new models like Astra and Fable zero-shotting robotics benchmarks, citing doubts about overfitting and limited novelty, while some accounts saw opportunities in applications or real step changes on private tests.

    Based on 24 sentiment-bearing replies from 22 accounts across 3 conversations.

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    8 Sources

    @AvivTamar1Just a reminder - robotic touch is not solved, and there is not much data to train on (currently). So there's still interesting work to do.
    @LerrelPintoRT @AvivTamar1: Just a reminder - robotic touch is not solved, and there is not much data to train on (currently). So there's still interes…
    @yoavgothe new wave of models also solve a couple of benchmarks/tasks we were working on, and which were hard for agents before. i can understand the despair. but they did not "zero shot" anything. i am pretty sure they were trained to the extreme on each and every one of these tasks.
    @xiaolonwRT @LerrelPinto: I’m sensing deep despair in academics over the past week. Astra, Fable, Muse are zero-shotting benchmarks in robotics & wo…
    @zehavoc@yoavgo Fun fact: we started building a benchmark on some educational multimodal tasks chstGPT from last year was absolutely lame. 6 months of work and annotation later, totally saturated.
    @Michael_J_Black@LerrelPinto We have to see this as an opportunity to jump ahead. We've been here before -- the field is constantly changing. There are many problems that remain unsolved and now we have new powerful tools that may help us solve them.
    @KostasPennQuite the opposite @LerrelPinto. For us it is the sweet lesson. We enjoy seeing hand-eye calibration and classic motion planning as well as other structured approaches applied by astra and fable! I think the despair is among VLA investors.
    @GeorgiaChal@KostasPenn @LerrelPinto Oh sweet priors and structure for the win and the scale! ❤️

    8 Sources

    @AvivTamar1Just a reminder - robotic touch is not solved, and there is not much data to train on (currently). So there's still interesting work to do.
    @LerrelPintoRT @AvivTamar1: Just a reminder - robotic touch is not solved, and there is not much data to train on (currently). So there's still interes…
    @yoavgothe new wave of models also solve a couple of benchmarks/tasks we were working on, and which were hard for agents before. i can understand the despair. but they did not "zero shot" anything. i am pretty sure they were trained to the extreme on each and every one of these tasks.
    @xiaolonwRT @LerrelPinto: I’m sensing deep despair in academics over the past week. Astra, Fable, Muse are zero-shotting benchmarks in robotics & wo…
    @zehavoc@yoavgo Fun fact: we started building a benchmark on some educational multimodal tasks chstGPT from last year was absolutely lame. 6 months of work and annotation later, totally saturated.
    @Michael_J_Black@LerrelPinto We have to see this as an opportunity to jump ahead. We've been here before -- the field is constantly changing. There are many problems that remain unsolved and now we have new powerful tools that may help us solve them.
    @KostasPennQuite the opposite @LerrelPinto. For us it is the sweet lesson. We enjoy seeing hand-eye calibration and classic motion planning as well as other structured approaches applied by astra and fable! I think the despair is among VLA investors.
    @GeorgiaChal@KostasPenn @LerrelPinto Oh sweet priors and structure for the win and the scale! ❤️