Toyota Research claims breakthrough in teaching robots new behaviors
Having been a member of the robot learning community both in grad school and now in industry, I'd actually like to rightfully attribute something here since it seems that TRI is (deservedly so, I will agree wholeheartely) receiving most of the praise:The core of these advancements are powered by Diffusion Policy [1], which Prof. Shuran Song's lab at Columbia (before she moved recently to Stanford) developed and pioneered. I'd suggest everyone to view the original project website [2], it has a ton of amazing real world challenging experiments.It was a community favorite for the Best Paper Award at the R:SS conference [3], this year. I remember our lab (and all other learning labs in our robotics department), absolutely dissecting this paper. I know of people who've entirely pivoted away from their projects involving behavior cloning/imitation learning, to this approach, which deals with multi-modal action spaces much more naturally than the aforementioned approaches.Prof. Song is an absolute rockstar in robotics right now, with several wonderful approaches that scale elegantly to the real world, including IRP [4] (which won Best Paper at R:SS 2022), FlingBot [5], Scaling Up Distilling Down [6] and much more. I recommend checking out her lab website too.[1] - https://arxiv.org/abs/2303.04137[2] - https://diffusion-policy.cs.columbia.edu/[3] - https://roboticsconference.org/program/awards/[4] - https://irp.cs.columbia.edu/[5] - https://flingbot.cs.columbia.edu/[6] - https://www.cs.columbia.edu/~huy/scalingup/