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

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-c...

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Bibliographic Details
Published inarXiv.org
Main Authors Ma, Xiao, Patidar, Sumit, Haughton, Iain, James, Stephen
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.03.2024
Subjects
Diffusers
End effectors
Kinematics
Robot control
Robot dynamics
Task planning (robotics)
Trajectory optimization
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Summary: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.
ISSN:2331-8422