Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments

Jiahui Yang, Jason Jingzhou Liu, Yulong Li, Youssef Khaky, Kenneth Shaw, Deepak Pathak

Keywords: Motion Planning, Robot Learning, Reactive Control

Abstract:

Generating collision-free motion in dynamic, partially
observable environments is a fundamental challenge for robotic
manipulators. Classical motion planners can compute globally
optimal trajectories but require full environment knowledge
and are typically too slow for dynamic scenes. Neural motion
policies offer a promising alternative by operating in closed-loop
directly on raw sensory inputs but often struggle to generalize in
complex or dynamic settings. We propose Deep Reactive Policy
(DRP), a visuo-motor neural motion policy designed for reactive
motion generation in diverse dynamic environments, operating
directly on point cloud sensory input. At its core is IMPACT, a
transformer-based neural motion policy pretrained on 10 million
generated expert trajectories across diverse simulation scenarios.
We further improve IMPACT’s static obstacle avoidance through
iterative student-teacher finetuning. We additionally enhance
the policy’s dynamic obstacle avoidance at inference time using
DCP-RMP, a locally reactive goal-proposal module. We evaluate
DRP on challenging tasks featuring cluttered scenes, dynamic
moving obstacles, and goal obstructions. DRP achieves strong
generalization, outperforming prior classical and neural methods
in success rate across both simulated and real-world settings.
Video results available at deep-reactive-policy.com.

Submission Number: 8