Humanoid Robot Fall Recovery

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13 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Active Reachability Learning for Humanoid Recoverability Monitoring
Oct 23, 2026Zeyuan Feng, Baljeet Singh, Sarthak Ranjeet Kaingade +2Reachability Analysis Humanoid Robot Fall Recovery
Stanford University · Amazon
Test-Time Motion Steering for Perceptive Humanoid Fall Recovery
Oct 21, 2026Osher Azulay, Cheng-Lin Hsieh, Stella X. YuHumanoid Robot Fall Recovery Humanoid Robot Control
University of Michigan, Ann Arbor
KungfuAthleteBot: learning high-dynamic humanoid motion from video with unified robust recovery
Oct 2, 2026Zhongxiang Lei, Lulu Cao, Xuyang Wang +3RL for Robotics Humanoid Robot Fall Recovery
Beijing Institute of Technology · Equal contribution · QIYUAN Lab
Reactive Humanoid Multi-Contact Using Learned Stability Models
Sep 30, 2026Stephen McCrory, Beomyeong Park, Nicholas Kitchel +2Humanoid Robot Locomotion Humanoid Robot Control
Florida Institute for Human and Machine Cognition · University of West Florida
Demonstration-Guided Humanoid Stand-Up on an Emulated Deformable Surface
Aug 21, 2026Aniruddh Kushwah, Vyankatesh Ashtekar, Ashish DuttaHumanoid Robot Fall Recovery Humanoid Robotics
Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur, Uttar Pradesh, India
StableMimic: Smooth Human-Like Recovery for Humanoid Motion Tracking - Learning Beyond the Tracking Distribution for Structured Post-Fall Behavior
Aug 3, 2026Weihao Wu, Ming Huang, Ruofei Liu +3Robot Failure Recovery Humanoid Robot Fall Recovery
Southern University of Science and Technology (SUSTech), Shenzhen, China.
Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids
Jun 11, 2026Xiao Ren, Yuhui Yang, Zongbiao Weng +2RL for Robotics Reinforcement Learning
Southern University of Science and Technology, Shenzhen 518055, China
CG-MuTra: Continuously-Gated Multi-Terrain Adaptive Recovery for Unified Humanoid Fall Recovery and Locomotion
Jun 8, 2026Xiaoyu Xu, Zhiming Chen, Yuenan Zhao +2Humanoid Robot Locomotion Robot Failure Recovery
School of Control Science and Engineering, Shandong University, Jinan, 250061, China · Key Laboratory of Machine Intelligence and System Control, Ministry of Education, China
Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors
May 18, 2026Yidan Lu, Yichao Zhong, Liu Zhao +2Humanoid Robot Locomotion Robot Failure Recovery
The University of Hong Kong, Hong Kong SAR, China.
RecoverFormer: End-to-End Contact-Aware Recovery for Humanoid Robots
Apr 24, 2026Zihui LiuHumanoid Robotics Humanoid Robot Control
Stanford University
Embedding Classical Balance Control Principles in Reinforcement Learning for Humanoid Recovery
Mar 9, 2026Nehar Poddar, Stephen McCrory, Luigi Penco +3Humanoid Robot Fall Recovery Humanoid Robot Control
Florida Institute for Human and Machine Cognition, Pensacola, FL, USA. · University of West Florida, Pensacola, FL, USA.
Unified Perceptive Fall Safety for Humanoids
Feb 18, 2026Osher Azulay, Zhengjie Xu, Andrew Scheffer +1Humanoid Robot Fall Recovery Humanoid Robot Control
University of Michigan, Ann Arbor
KungfuAthleteBot: Learning High-Dynamic Motion from Videos with Unified Robust Recovery
Feb 14, 2026Zhongxiang Lei, Lulu Cao, Xuyang Wang +3Humanoid Robot Fall Recovery Humanoid Robotics
Beijing Institute of Technology · QIYUAN Lab

Oct 23, 2026J/K move · Enter open · S save

Active Reachability Learning for Humanoid Recoverability Monitoring

Zeyuan Feng, Baljeet Singh, Sarthak Ranjeet Kaingade, Somil Bansal+1

Stanford University · Amazon

Reachability Analysis Humanoid Robot Fall Recovery Uncertainty-Aware Exploration Uncertainty Estimation in RL

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Deploying humanoid task policies requires reliable runtime monitors that anticipate unrecoverable falls in time to trigger a fallback. We formulate this task as learning the stochastic, finite-horizon recoverability envelope of a given fallback policy. Synthesizing this monitor is challenging: existing methods assume training data naturally covers the recoverability envelope, yet acquiring such data remains a major bottleneck for contact-rich systems. Consequently, they recover only a fraction of the true envelope, leaving them prone to optimistic hallucinations for out-of-distribution (OOD) inputs. We resolve this via a unified framework that iterates between value learning and active exploration. Data exploration is reframed as a reach-avoid problem, where an uncertainty-guided explorer systematically uncovers unseen states while avoiding confidently unrecoverable regions. This loop is further supported by a combined Temporal Difference and Monte Carlo objective for accurate in-distribution value prediction and reliable uncertainty estimation. By explicitly treating the remaining high-uncertainty regions as unrecoverable, the monitor mitigates optimistic OOD hallucinations. Evaluated on stochastic benchmarks and a 29-DoF Unitree G1 humanoid, our approach drastically improves monitor coverage and accuracy and successfully transfers zero-shot to real-world hardware.