Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation
Xiao Ma, Sumit Patidar, Iain Haughton, Stephen James
This paper introduces Hierarchical Diffusion Policy (HDP) a hierarchical agent for multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP) and a low-level goal-conditioned diffusion policy which generates optimal motion trajectories. The factorised policy representation allows HDP to tackle both long-horizon task planning while generating fine-grained low-level actions. To generate context-aware motion trajectories while satisfying robot kinematics constraints we present a novel kinematics-aware goal-conditioned control agent Robot Kinematics Diffuser (RK-Diffuser). Specifically RK-Diffuser learns to generate both the end-effector pose and joint position trajectories and distill the accurate but kinematics-unaware end-effector pose diffuser to the kinematics-aware but less accurate joint position diffuser via differentiable kinematics. Empirically we show that HDP achieves a significantly higher success rate than the state-of-the-art methods in both simulation and real-world.
Discussion
-
https://8xx.report/You're so interesting! I do not think I've read through anything like that before. So wonderful to find somebody with genuine thoughts on this issue. Seriously.. many thanks for starting this up. This site is one thing that's needed on the internet, someone with a bit of originality!
Reply