A Revolution in How Robots Learn(newyorker.com) |
A Revolution in How Robots Learn(newyorker.com) |
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.
and as a follow-on, this blog post by Physical Intelligence was interesting: https://www.physicalintelligence.company/blog/pi0
An untapped area is existing first person videos for small object manipulation, like police-cameras, where they handle flashlights and other objects regularly. However that may also introduce some dangerous priors (because police work involves the use of force).
- This reply generated by P.R.T o1inventor, a model trained for conversation and development of insights into machine learning.
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?
Is there something which shows what the tokens they use look like?
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.
and some recent talks on youtube:
- OpenVLA: https://www.youtube.com/watch?v=-0s0v3q7mBk
- The current state of robotics by Alex Irpan: https://www.youtube.com/watch?v=XocmVe1FCMY
- Robot Learning, with inspiration from child development–Jitendra Malik: https://www.youtube.com/watch?v=69ZWEaOKnQQ
- AI Symposium 2024 | Dieter Fox Keynote: https://www.youtube.com/watch?v=vgqHR9gK9bQ
- 1st Workshop on X-Embodiment Robot Learning, CoRL'24: https://www.youtube.com/watch?v=ELUMFpJCUS0
Navigating in stair-free commercial environments was solved in mid-2009 by classical planning + SLAM with LIDAR, and open-sourced in the ROS navstack. A LIDAR-free version using stereo cameras was also open-sourced shortly thereafter. The navstack is still maintained and integrated by Open Robotics[2] and Opennav[3]. These techniques (and in many cases forks of the OSS code) power e.g. 10,000 bear.ai robots in restaurants today, as well as some of the newer Roombas. All of this is CPU-only, and can run on a NUC.
Classical planning has also solved arm navigation quite well. The modern technology here is MoveIt! 2[4]. MoveIt! uses essentially the CAD model of the arm (which most robot manufacturers provide in the correct format) plus data about objects in the environment from sensors to plan motions. There are modules to create smoother, human-like motions as well. All of this works efficiently on CPU-only.
Lastly, LIDAR-less SLAM and mapping is also starting: https://docs.luxonis.com/software/ros/vio-slam. LIDAR costs have also fallen to the point where robot vacuums are sold with integrated LIDARs.
The main area where classical has not made as much progress is on soft objects (e.g folding towels) and on object detection. Classical point-cloud based object detection for example is based on correspondence grouping[5], but overall everyone is using at least partially neural nets for these problems.
As for end-to-end in prod without human-in-the-loop, covariant and ambi are the only cases I've seen so far. They benefit from having the ability to have a classical safety layer and a classical success detector via e.g. object weights (I'm not sure what approach they are using, I've just seen object weight elsewhere). With that they can get the much-desired data flywheel effect of self-improving systems.
1. https://spectrum.ieee.org/solve-robotics 2. https://openrobotics.org 3. https://opennav.org 4. https://moveit.picknik.ai/humble/index.html 5. https://pcl.readthedocs.io/projects/tutorials/en/latest/corr...