The mistake here was overestimating the capabilities of existing learning algorithms. It should be self-evident that if we want to manufacture robots using processes that are not exact, or if aspects of their bodies change over time, then there is no way around continual adaptation. Tendon-driven hands introduce both of these challenges. When your robot bodies are not exact, then just finding a performant policy on one body is not enough. Maintaining performance over time and successfully transferring the same policy to multiple bodies are both non-trivial tasks. This is something that Keen's Physical Atari setup clearly demonstrated. Historically, the robotics industry has sidestepped this issue by making robots with tight tolerances and by maintaining those tolerances. It seems like Figure is headed in that direction as well. Ultimately, the right solution is to fix the learning algorithms, organize the knowledge of the robot as self-verifiable subproblems, and have the robot maintain the correctness of this knowledge on its own through continual learning. Everything else is just a band-aid.

Last month, @RichardSSutton and I left Keen to do our own thing. I had ~two absolutely wonderful years at Keen, and I learned a lot working with John, Gloria, Joseph, and the rest of the team. If you want to work on some of the foundational unsolved problems in AI, such as continual learning, then I would strongly recommend applying to Keen. Going forward, Rich and I have founded a small company called Oak Lab. We have a fairly complete roadmap to building animal-like intelligence that learns purely from its own experience (the OaK architecture), and Oak Lab is going to follow this roadmap aggressively with a small, focused team. We will be sharing our progress often and aim to build a prototype of the complete OaK architecture in the next few years. A successful prototype will be closer to a baby learning in its first year than it will be to any of the current AI systems. Our strategy is to demonstrate the limitations of current methods in simple settings, and then work out algorithms that overcome these limitations in a domain-independent way. Only after we have made sufficient progress on the core algorithms will we build large-scale artifacts. If you are interested in learning about some of the specifics of our approach, then follow @oaklab_ai. We will be sharing more details in the coming weeks and months.