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.