Humanoid Safe Stop via Learned Stoppability Value

2026-09-02

AI Summary

本文通过学习停止策略和可停性估计器，解决人形机器人在紧急停止时的安全问题，确保只有在安全可行的情况下才执行停止。

Abstract

Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feasible from the current state. We cast emergency stopping as a reach-avoid problem and propose Safe-Stop, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators. The estimators are complementary: a stop-probability estimator supervised by the actual outcomes of the fixed stop policy, and a reach-avoidance estimator supervised by a Hamilton-Jacobi backup over physical state. The first captures emergent stopping behavior of the learned controller; the second provides a complementary recoverability signal. Because the stop policy and estimators do not depend on the behavior policy that preceded the stop command, they transfer across diverse upstream tasks without retraining. At deployment, the two estimates are combined: Safe-Stop commits to the stop only when both estimators indicate that stopping remains feasible, otherwise it hands off to a fall policy, instantiated as a damping fallback. This agreement check yields decisions that are robust without sacrificing reactivity.

Problem

• emergency stop
• humanoid robots
• stoppability
• safe stop
• reach-avoid problem

Innovation

• learned stop policy
• stoppability estimators
• reach-avoid problem
• task-agnostic framework
• fall policy