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

arXiv:2403.03890v1 Announce Type: cross
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 …

abstract agent arxiv cs.ai cs.cv cs.lg cs.ro diffusion hierarchical low manipulation next paper planning policy robotic robotic manipulation type