Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation

Xiao Ma, Sumit Patidar, Iain Haughton, Stephen James; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 18081-18090

Abstract


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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Ma_2024_CVPR, author = {Ma, Xiao and Patidar, Sumit and Haughton, Iain and James, Stephen}, title = {Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {18081-18090} }