Avoid Everything: Model-Free Collision Avoidance with Expert-Guided Fine-Tuning

Adam Fishman, Aaron Walsman, Mohak Bhardwaj, Wentao Yuan, Balakumar Sundaralingam, Byron Boots, Dieter Fox

Keywords: Imitation Learning, Robotics, Collision Avoidance, Fine Tuning, Motion Planning

TL;DR: Avoid Everything is a novel end-to-end system for generating collision-free motion toward a goal that successfully solves 63% of challenging problems where the previous state of the art method fails.

Abstract: The world is full of clutter. In order to operate effectively in uncontrolled, real world spaces, robots must navigate safely by executing tasks around obstacles while in proximity to hazards. Creating safe movement for robotic manipulators remains a long-standing challenge in robotics, particularly in environments with partial observability. In partially observed settings, classical techniques often fail. Learned end-to-end motion policies can infer correct solutions in these settings, but are as-yet unable to produce reliably safe movement when close to obstacles. In this work, we introduce Avoid Everything, a novel end-to-end system for generating collision-free motion toward a target, even targets close to obstacles. Avoid Everything consists of two parts: 1) Motion Policy Transformer (M$\pi$Former), a transformer architecture for end-to-end joint space control from point clouds, trained on over 1,000,000 expert trajectories and 2) a fine-tuning procedure we call Refining on Optimized Policy Experts (ROPE), which uses optimization to provide demonstrations of safe behavior in challenging states. With these techniques, we are able to successfully solve over 63% of reaching problems that caused the previous state of the art method to fail, resulting in an overall success rate of over 91\% in challenging manipulation settings.

Video: https://youtu.be/QS6-opgZDjA

Website: https://avoid-everything.github.io

Code: https://github.com/fishbotics/avoid-everything

Student Paper: yes

Submission Number: 314