7 ms·
A Revolution in How Robots Learn
- x11antiek 2y agohttps://archive.is/fsuxe https://archive.is/fsuxe
- Animats 2y agoA research result reported before, but, as usual, the New Yorker has better writers. Is there something which shows what the tokens they use look like?
- falcor84 2y ago> the New Yorker has better writers. Really? I suppose it's very subjective, but I find their style, both in this article and in general to be unbearably long - almost as if their journalists enjoy writing for the sake of writing, with the transmission of information being a minor concern.
- codr7 2y agoOh my, that has to be one of the worst jobs ever invented.
- m_ke 2y agoI did a review of state of the art in robotics recently in prep for some job interviews and the stack is the same as all other ML problems these days, take a large pretrained multi modal model and do supervised fine tuning of it on your domain data. In this case it's "VLA" as in Vision Language Action models, where a multimodal decoder predicts action tokens and "behavior cloning" is a fancy made up term for supervised learning, because all of the RL people can't get themselves to admit that supervised learning works way better than reinforcement learning in the real world. Proper imitation learning where a robot learns from 3rd person view of humans doing stuff does not work yet, but some people in the field like to pretend that teleoperation and "behavior cloning" is a form of imitation learning.
- ibash 2y agoWould you mind sharing which readings you found most useful in that review?
- chillel 2y agoyeah, I'd love to see your list
- deleted 2y ago[deleted]
- chillel 2y agojust coming back to this thread, this paper is quite a good read: https://arxiv.org/html/2406.09246v3#S3 https://arxiv.org/html/2406.09246v3#S3 and as a follow-on, this blog post by Physical Intelligence was interesting: https://www.physicalintelligence.company/blog/pi0 https://www.physicalintelligence.company/blog/pi0
- m_ke 2y agohey just got back on and the papers you shared are the main works that I was about to link. There's also a new VLA paper fro waymo https://arxiv.org/abs/2410.23262v2 https://arxiv.org/abs/2410.23262v2 and some recent talks on youtube: - OpenVLA: https://www.youtube.com/watch?v=-0s0v3q7mBk https://www.youtube.com/watch?v=-0s0v3q7mBk - The current state of robotics by Alex Irpan: https://www.youtube.com/watch?v=XocmVe1FCMY https://www.youtube.com/watch?v=XocmVe1FCMY - Robot Learning, with inspiration from child development–Jitendra Malik: https://www.youtube.com/watch?v=69ZWEaOKnQQ https://www.youtube.com/watch?v=69ZWEaOKnQQ - AI Symposium 2024 | Dieter Fox Keynote: https://www.youtube.com/watch?v=vgqHR9gK9bQ https://www.youtube.com/watch?v=vgqHR9gK9bQ - 1st Workshop on X-Embodiment Robot Learning, CoRL'24: https://www.youtube.com/watch?v=ELUMFpJCUS0 https://www.youtube.com/watch?v=ELUMFpJCUS0
- chillel 2y agobrilliant, thank you
- josefritzishere 2y agoThere's a big asterisk on the word "learn" in that headline.
- ratedgene 2y agoHey, I wonder if we can use LLMs to learn learning patterns, I guess the bottleneck would be the curse of dimensionality when it comes to real world problems, but I think maybe (correct me if I'm wrong) geographic/domain specific attention networks could be used. Maybe it's like: 1. Intention, context 2. Attention scanning for components 3. Attention network discovery 4. Rescan for missing components 5. If no relevant context exists or found 6. Learned parameters are initially greedy 7. Storage of parameters gets reduced over time by other contributors I guess this relies on there being the tough parts: induction, deduction, abductive reasoning. Can we fake reasoning to test hypothesis that alter the weights of whatever model we use for reasoning?
- ratedgene 2y agoMaybe I'm just complicating unsupervised reinforcement learning, and adding central authorities for domain specific models.
- deleted 2y ago[deleted]
- nobodywillobsrv 2y agoAnyone find is suspcious that all these paywalled fluff tech legacy media articles keep on ending up on hn? Feels like an op. Who in tech actually reads NYT for example?
- barrenko 2y agoPeople procrastinating on their ML job.
- drcwpl 2y agoOne particularly fascinating aspect of this essay is the comparison between human motor learning and robotic dexterity development, particularly the concept of “motor babbling.” The author highlights how babies use seemingly random movements to calibrate their brains with their bodies, drawing a parallel to how robots are being trained to achieve precise physical tasks. This framing makes the complexity of robotic learning, such as a robot tying shoelaces or threading a needle, more relatable and underscores the immense challenge of replicating human physical intelligence in machines. For me it is also a vivid reminder of how much we take our own physical adaptability for granted.
- mistrial9 2y agomachine learning is not equal to human infant development, full stop.