FAULT-AWARE POLICY REUSE VIA SKILL LIBRARIES FOR ADAPTIVE ROBOT LEARNING

Recent advances in embodied agent adaptation have largely focused on changes in the task or environmental dynamics. However, a robot's own dynamics can also change mid-deployment due to a hardware failure. Consequently, policies trained under nominal dynamics can degrade significantly, even when the task/environment remains unchanged. Existing fault-tolerant approaches either require fault-specific engineering or training a single monolithic policy to generalize across diverse changes in robot dynamics. Policy reuse offers a more targeted alternative, but existing methods are not explicitly organized around the robot's fault state for subsequent adaptation. We propose Skill Library, a framework that organizes reusable policies by fault condition. We first pretrain a collection of lightweight, fault-indexed policies offline. During deployment, when the detected fault is represented in the library, the matched policy is retrieved for direct reuse; when the fault is unseen, stored policies serve as initialization for online adaptation. Our fault-aware Skill Library focuses on changes in the robot's dynamics with policies indexed by the affected joint and fault severity. We evaluate our framework across four manipulation environments on a 7-DOF Franka Panda in MuJoCo. Our results demonstrate that our framework consistently improves adaptation sample efficiency over both training from scratch and fine-tuning from the nominal policy, reducing the recovery interaction budget and improving final success rates. Moreover, experiments on unseen compound faults show that a library of single-joint policies alone can provide useful initialization for complex, multi-joint failures, extending the fault adaptation beyond the conditions explicitly represented in the offline library.