Humanoid Safe Stop via Learned Stoppability Value
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....
| Authors | Junfeng Long, Pieter Abbeel, Koushil Sreenath et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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