Residual Fault Adaptation for Dexterous In-Hand Manipulation Under Runtime Joint Faults

2026-09-15

AI Summary

研究提出残差故障适应方法解决灵巧手操作中运行时关节故障问题，通过保留健康教师模型和训练循环残差策略来推断校正动作。

Abstract

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 mode, affected joint, severity, and onset time, while adaptive sampling increases the frequency of fault modes associated with lower recent performance. A frozen Direct FIDR policy provides a distributional reference only on fault-active training samples and is absent from deployment. The deployed controller receives neither fault labels nor controller-switching signals. Simulation experiments on the dexterous hand indicate that RFA can improve manipulation performance relative to the healthy policy under a fixed mixed-fault protocol. Real-robot experiments with software-injected faults further demonstrate zero-shot deployment of the learned adaptation policy.

Problem

• dexterous in-hand manipulation
• runtime joint faults
• manipulation performance

Innovation

• residual fault adaptation
• fault-injection domain randomization
• adaptive sampling