From Imitation to Refinement – Residual RL for Precise Visual Assembly

Lars Lien Ankile, Anthony Simeonov, Idan Shenfeld, Marcel Torne Villasevil, Pulkit Agrawal

Keywords: Residual Learning, Robotic Assembly, Combining BC and RL

Abstract: Behavior cloning (BC) currently stands as a dominant paradigm for learning real-world visual manipulation. However, in tasks that require locally corrective behaviors like multi-part assembly, learning robust policies purely from human demonstrations remains challenging. Reinforcement learning (RL) can mitigate these limitations by allowing policies to acquire locally corrective behaviors through task reward supervision and exploration. This paper explores the use of RL fine-tuning to improve upon BC-trained policies in precise manipulation tasks. We analyze and overcome technical challenges associated with using RL to directly train policy networks that incorporate modern architectural components like diffusion models and action chunking. We propose training residual policies on top of frozen BC-trained diffusion models using standard policy gradient methods and sparse rewards. Our experimental results demonstrate that this residual learning framework can significantly improve success rates beyond the base BC-trained models in high-precision assembly tasks by learning corrective actions. We also show that by combining our residual learning approach with teacher-student distillation and visual domain randomization, our method can enable learning real-world policies for robotic assembly directly from RGB images.

Submission Number: 30