Most coverage of humanoid robotics has understandably focused on hardware design. Given the frequency with which their developers toss around the phrase “general purpose humanoids,” more attention ought to be paid to the first bit. After decades of single-purpose systems, the jump to more generalized systems will be a big one. We’re just not there yet. The push to produce a robotic intelligence that can fully leverage the wide breadth of movements opened up by bipedal humanoid design has been a key topic for researchers. The use of generative AI in robotics has been a white-hot subject recently, as well. New research out of MIT points to how the latter might profoundly affect the former. One of the biggest challenges on the road to general-purpose systems is training. We have a solid grasp on best practices for training humans how to do different jobs. The approaches to robotics, while promising, are fragmented. There are a lot of promising methods, including reinforcement and imitation learning, but future solutions will likely involve combinations of these methods, augmented by generative AI models. One of the prime use cases suggested by the MIT team is the ability to collate relevant information from these small, task-specific datasets. The method has been dubbed policy composition (PoCo). Tasks include useful robot actions like pounding in a nail and flipping things with a spatula.
Full report : Generative AI takes robots a step closer to general purpose.
