Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?

TL;DR

This paper explores using model-based planning to generate training data for contact-rich manipulation, addressing the difficulty of collecting demonstrations and improving policy learning and transfer.

Contribution

It introduces modifications to sampling-based planners to produce more consistent demonstrations and combines them with diffusion-based behavior cloning for better manipulation policies.

Findings

Effective policy learning for contact-rich tasks

Zero-shot transfer to real hardware achieved

Modified planning improves demonstration quality

Abstract

The tremendous success of behavior cloning (BC) in robotic manipulation has been largely confined to tasks where demonstrations can be effectively collected through human teleoperation. However, demonstrations for contact-rich manipulation tasks that require complex coordination of multiple contacts are difficult to collect due to the limitations of current teleoperation interfaces. We investigate how to leverage model-based planning and optimization to generate training data for contact-rich dexterous manipulation tasks. Our analysis reveals that popular sampling-based planners like rapidly exploring random tree (RRT), while efficient for motion planning, produce demonstrations with unfavorably high entropy. This motivates modifications to our data generation pipeline that prioritizes demonstration consistency while maintaining solution diversity. Combined with a diffusion-based goal-conditioned BC approach, our method enables effective policy learning and zero-shot transfer to hardware for two challenging contact-rich manipulation tasks.