mlsanityData to 14 Oct 2026

TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards

arXiv 2602.05596 ↗First posted 5 Feb 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

With the growing employment of learning algorithms in robotic applications, research on reinforcement learning for bipedal locomotion has become a central topic for humanoid robotics. While recently published contributions achieve high success rates in locomotion tasks, scarce attention has been devoted to the development of methods that enable to handle hardware faults that may occur during the locomotion process. However, in real-world settings, environmental disturbances or sudden occurrences of hardware faults might yield severe consequences. To address these issues, this paper presents TOLEBI (A faulT-tOlerant Learning framEwork for Bipedal locomotIon) that handles faults on the robot during operation. Specifically, joint locking, power loss and external disturbances are injected in simulation to learn fault-tolerant locomotion strategies. In addition to transferring the learned policy to the real robot via sim-to-real transfer, an online joint status module incorporated. This module enables to classify joint conditions by referring to the actual observations at runtime under real-world conditions. The validation experiments conducted both in real-world and simulation with the humanoid robot TOCABI highlight the applicability of the proposed approach. To our knowledge, this manuscript provides the first learning-based fault-tolerant framework for bipedal locomotion, thereby fostering the development of efficient learning methods in this field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 35 references that are in the map. First posted on arXiv 5 Feb 2026.

Builds on

16 of 35 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning25 Sep
  2. 2024Whole-body Humanoid Robot Locomotion with Human Reference28 Feb
  3. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  5. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
  6. 2021Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robotscited by 116
  7. 2019Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learningcited by 32
  8. 2023Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfercited by 22
  9. 2021Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Controlcited by 5
  10. 2024Multi-Task Learning of Active Fault-Tolerant Controller for Leg Failures in Quadruped robotscited by 5
  11. 2024Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Maskingcited by 4
  12. 2023Adaptive Control Strategy for Quadruped Robots in Actuator Degradation Scenarioscited by 2
1 more
  1. 2022Saving the Limping: Fault-tolerant Quadruped Locomotion via Reinforcement Learningcited by 1

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  1. 2024Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey
  2. 2026Mixture-of-Experts RL for Fault-Tolerant Legged Locomotion ↗
  3. 2025Robust RL Control for Bipedal Locomotion with Closed Kinematic Chains
  4. 2025Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot
  5. 2026Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds
  6. 2026Learning Fault-Tolerant Locomotion with Adaptive Gait Timing
  7. 2025Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning
  8. 2026Model-Informed Safe Reinforcement Learning for Bipedal Locomotion via Step-to-Step Prediction