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arXiv · 2610.41663

ResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies

Kefei Zhu·Lingyun Xu·Yue Xin·Xiaoqi Li·Caixin Guo·Xincheng Du·Yuanpei Chen·Yaodong Yang·Hao Dong
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Abstract

Continual adaptation to new tasks is essential for robots deployed in the real world, but adapting pre-trained policies remains challenging due to parameter growth and the need for continual demonstration collection. Parameter-Efficient Continual Fine-Tuning (PECFT) isolates task-specific updates, but adapter-per-task designs require demonstrations and grow with the task set. We propose ResMerge, a two-stage framework that learns mergeable residual policies atop a frozen pre-trained policy: residual reinforcement learning (RL) acquires task-specific corrections without additional expert demonstrations during adaptation, and parameter-space merging consolidates compatible residuals to reduce stored parameters. We introduce Residual-Field Consistency (RFC), a simple, bounded, and interpretable metric that assesses compatibility in residual action-field space by comparing correction direction and magnitude. Experiments on LIBERO across two backbones and on a real-world Franka robot show improved continual learning performance over strong PECFT baselines, while selective merging reduces the residual-parameter footprint. Project website: https://resmerge.github.io/.

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Kefei Zhu, Lingyun Xu, Yue Xin, Xiaoqi Li, Caixin Guo, Xincheng Du, Yuanpei Chen, Yaodong Yang, Hao Dong. 2026-10-30. ResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies. https://arxiv.org/abs/2610.41663

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