Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation
<|reference_start|>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-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.<|reference_end|>
@article{ma2024hierarchical,
    title={Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic
  Manipulation},
    author={Xiao Ma, Sumit Patidar, Iain Haughton, Stephen James},
    journal={arXiv preprint arXiv:2403.03890},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03890},
    primaryClass={cs.RO cs.AI cs.CV cs.LG}
}