Paper Code Twitter Simulation Data Real Robot Data 3D Diffusion Policy Generalizable Visuomotor Policy Learning via Simple 3D Representations Abstract. Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3) , a novel visual imitation learning approach that incorporates the power of 3D visu
Effectiveness. We evaluate DP3 on 72 simulated tasks and 4 real-world tasks and observe that DP3 is able to achieve surprising effectiveness in diverse simulated and real-world tasks, including both high-dimensional and low-dimensional control tasks, with a practical inference speed. Notably, for 67 out of 72 simulated tasks, we only use 10 demonstrations; for 4 challenging real-world tasks, we only use 40 demonstrations.
Generalization with few data. We use MetaWorld Reach as an example task, given only • 5 demonstrations. We evaluate 1000 times to cover the 3D space and visualize • successful evaluation points. DP3 learns the generalizable skill in 3D space; Diffusion Policy and IBC only succeed in partial space; BCRNN fails to learn such a simple skill with limited data. Check out more generalization abilities of DP3!