Towards Safe Imitation Learning via Potential Field-Guided Flow Matching

TL;DR

This paper introduces PF2MP, a novel imitation learning approach that integrates potential fields with flow matching to generate safer motions in obstacle-rich environments, improving safety without sacrificing task success.

Contribution

The paper proposes a new method combining potential fields with flow matching for safer imitation learning in complex environments, addressing safety concerns overlooked by existing models.

Findings

PF2MP reduces collisions significantly compared to baselines.

The approach is effective in both simulation and real-world tasks.

It improves safety in navigation and manipulation scenarios.

Abstract

Deep generative models, particularly diffusion and flow matching models, have recently shown remarkable potential in learning complex policies through imitation learning. However, the safety of generated motions remains overlooked, particularly in complex environments with inherent obstacles. In this work, we address this critical gap by proposing Potential Field-Guided Flow Matching Policy (PF2MP), a novel approach that simultaneously learns task policies and extracts obstacle-related information, represented as a potential field, from the same set of successful demonstrations. During inference, PF2MP modulates the flow matching vector field via the learned potential field, enabling safe motion generation. By leveraging these complementary fields, our approach achieves improved safety without compromising task success across diverse environments, such as navigation tasks and robotic manipulation scenarios. We evaluate PF2MP in both simulation and real-world settings, demonstrating its effectiveness in task space and joint space control. Experimental results demonstrate that PF2MP enhances safety, achieving a significant reduction of collisions compared to baseline policies. This work paves the way for safer motion generation in unstructured and obstaclerich environments.