ResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies
Overview
This research introduces ResMerge, a framework designed for the continual adaptation of pre-trained robot policies. It employs a two-stage approach involving residual reinforcement learning to acquire task-specific corrections without needing new expert demonstrations, followed by parameter-space merging to consolidate compatible residuals and reduce storage requirements. The method includes a metric called Residual-Field Consistency (RFC) to assess compatibility in action-field space. Experiments conducted on the LIBERO benchmark across two different backbones and on a real-world Franka robot demonstrate that ResMerge achieves improved continual learning performance compared to strong Parameter-Efficient Continual Fine-Tuning (PECFT) baselines, while also reducing the residual-parameter footprint through selective merging.
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- arXiv Robotics — research abstractsSignalResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies
This research introduces ResMerge, a framework designed for the continual adaptation of pre-trained robot policies. It employs a two-stage approach involving residual reinforcement learning to acquire task-specific corrections without needing new expert demonstrations, followed by parameter-space merging to consolidate compatible residuals and reduce storage requirements. The method includes a metric called Residual-Field Consistency (RFC) to assess compatibility in action-field space. Experiments conducted on the LIBERO benchmark across two different backbones and on a real-world Franka robot demonstrate that ResMerge achieves improved continual learning performance compared to strong Parameter-Efficient Continual Fine-Tuning (PECFT) baselines, while also reducing the residual-parameter footprint through selective merging.
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