mlsanityData to 14 Oct 2026

Humanoid Safe Stop via Learned Stoppability Value

arXiv 2609.02358 ↗First posted 2 Sep 2026cs.RO cs.LG cs.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 42 references that are in the map. First posted on arXiv 2 Sep 2026.

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References · 27

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  11. 2024Agile But Safe: Learning Collision-Free High-Speed Legged Locomotioncited by 73
  12. 2016Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Modelcited by 54
15 more
  1. 2021Robust Control Barrier-Value Functions for Safety-Critical Controlcited by 51
  2. 2024Learning Humanoid Locomotion with Perceptive Internal Modelcited by 50
  3. 2021Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learningcited by 49
  4. 2022Safe Reinforcement Learning for Legged Locomotioncited by 35
  5. 2024Solving Minimum-Cost Reach Avoid using Reinforcement Learningcited by 14
  6. 2024Learning H-Infinity Locomotion Controlcited by 11
  7. 2025VB-Com: Learning Vision-Blind Composite Humanoid Locomotion Against Deficient Perceptioncited by 10
  8. 2025Towards Adaptable Humanoid Control via Adaptive Motion Trackingcited by 8
  9. 2025SafeFall: Learning Protective Control for Humanoid Robotscited by 8
  10. 2017Learning a Unified Control Policy for Safe Fallingcited by 4
  11. 2025Unified Humanoid Fall-Safety Policy from a Few Demonstrationscited by 4
  12. 2025Robot Crash Course: Learning Soft and Stylized Fallingcited by 4
  13. 2023Fall Prediction for Bipedal Robots: The Standing Phasecited by 3
  14. 2026Learning Safe-Stoppability Monitors for Humanoid Robotscited by 2
  15. 2018Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimizationcited by 1

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