SOURCE-LINKED INTELLIGENCE
Residual Fault Adaptation for Dexterous In-Hand Manipulation Under Runtime Joint Faults
Dexterous in-hand manipulation requires coordinated control of multiple actuated joints, and a runtime joint fault can abruptly disrupt the contact configuration required for successful manipulation. In this work, we propose residual fault adaptation (RFA), a teacher-anchored framework for compensating for hidden command-channel faults. RFA retains a frozen healthy teacher to provide nominal behavior and trains a recurrent residual policy to infer corrective actions from proprioceptive and command-response history. During training, fault-injection domain randomization (FIDR) varies the fault m
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T16:34:45.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.