# Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

arXiv: https://arxiv.org/abs/2608.07328 · HTML: https://mlsanity.com/paper/2608.07328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Rapid Locomotion via Reinforcement Learning](https://mlsanity.com/paper/2205.02824/) (2022)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://mlsanity.com/paper/2405.10830/) (2024)
- [Fault-Aware Robust Control via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2011.08728/) (2020)
- [Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control](https://mlsanity.com/paper/2111.10005/) (2021)
- [Multi-Task Learning of Active Fault-Tolerant Controller for Leg Failures in Quadruped robots](https://mlsanity.com/paper/2402.08996/) (2024)
- [Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction](https://mlsanity.com/paper/2512.07464/) (2025)
- [Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Masking](https://mlsanity.com/paper/2403.00398/) (2024)
- [AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control](https://mlsanity.com/paper/2503.21401/) (2025)
- [DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains](https://mlsanity.com/paper/2502.05817/) (2025)
- [KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability](https://mlsanity.com/paper/2606.30243/) (2026)

## Built on by (most cited first, 1 of 1)

- [Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation](https://mlsanity.com/paper/2610.10934/) (2026)

## Abstract (arXiv)

Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadrupeds, where increased mass and tighter actuation limits reduce the feasibility of aggressive, high-frequency compensation strategies often observed on smaller platforms. In this work, we propose a deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss. The method employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive observations. We introduce a latent-alignment loss that encourages consistency between actor and critic representations. Additionally, we augment the action space with a learnable gait frequency parameter, enabling adaptive gait timing in response to terrain variations and actuator degradation without predefined faulty-leg strategies. The approach is validated in high-fidelity simulation on uneven terrain and real-world experiments on flat ground using a 68 kg quadruped robot.