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.