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arXiv Machine Learning By Junfeng Long, Pieter Abbeel, Koushil Sreenath, Roberto Horowitz, Guanya Shi, C. Karen Liu

Humanoid Safe Stop via Learned Stoppability Value

Read the original on arXiv Machine Learning →

The paper introduces Safe-Stop, a task‑agnostic framework for humanoid robots that determines whether an emergency stop command can be safely executed from the current state. It couples a learned stop policy with two complementary stoppability estimators: a stop‑probability estimator trained on outcomes of a fixed stop policy, and a reach‑avoidance estimator trained via Hamilton‑Jacobi backup. By requiring agreement between both estimators before committing to a stop, Safe‑Stop can robustly decide to stop or hand off to a fall‑damping fallback without needing retraining for different upstream tasks.

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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