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arXiv Machine Learning By Mohammad Khoshnazar, Mohammad Dehghani Tezerjani, Deyuan Qu, Zhiyuan Gao, Yanxiang Zhan, Jeroen Schafer, Andrew Melnik, Qing Yang, Michael Beetz

CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding

Read the original on arXiv Machine Learning →

CAPABLE is a capability‑aware policy adaptation framework that enables frozen vision‑language‑action (VLA) policies to recover from joint faults without task‑specific retraining or fault labels. It self‑infers each joint’s actual motion contribution using a temporal encoder, Jacobian grounding, and cross‑joint attention, then conditions a residual reinforcement learning policy to apply bounded corrections to the VLA arm action. Experiments on 28 LIBERO tasks show CAPABLE improves success rates from 24.8% to 59.3% on an actuator excluded from fault training, outperforming a global‑history baseline by 17.4 points while maintaining healthy performance and demonstrating transfer to unseen fault families and a physical Franka Panda robot.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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