# AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control

arXiv: https://arxiv.org/abs/2503.21401 · HTML: https://mlsanity.com/paper/2503.21401/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 27 Mar 2025.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments](https://mlsanity.com/paper/1910.07224/) (2019)
- [Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions](https://mlsanity.com/paper/2203.15103/) (2022)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models](https://mlsanity.com/paper/2308.15143/) (2023)
- [Robust Quadruped Jumping via Deep Reinforcement Learning](https://mlsanity.com/paper/2011.07089/) (2020)
- [Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning](https://mlsanity.com/paper/2106.09357/) (2021)
- [Learning to Navigate Sidewalks in Outdoor Environments](https://mlsanity.com/paper/2109.05603/) (2021)
- [Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion](https://mlsanity.com/paper/2109.06409/) (2021)
- [Robust Ladder Climbing with a Quadrupedal Robot](https://mlsanity.com/paper/2409.17731/) (2024)
- [Multi-Task Learning of Active Fault-Tolerant Controller for Leg Failures in Quadruped robots](https://mlsanity.com/paper/2402.08996/) (2024)
- [Adaptive Control Strategy for Quadruped Robots in Actuator Degradation Scenarios](https://mlsanity.com/paper/2312.17606/) (2023)

## Built on by (most cited first, 3 of 3)

- [Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion](https://mlsanity.com/paper/2507.01243/) (2025)
- [Learning Fault-Tolerant Locomotion with Adaptive Gait Timing](https://mlsanity.com/paper/2608.07328/) (2026)
- [Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation](https://mlsanity.com/paper/2610.10934/) (2026)

## Abstract (arXiv)

Quadrupedal robots can learn versatile locomotion skills but remain vulnerable when one or more joints lose power. In contrast, dogs and cats can adopt limping gaits when injured, demonstrating their remarkable ability to adapt to physical conditions. Inspired by such adaptability, this paper presents Action Learner (AcL), a novel teacher-student reinforcement learning framework that enables quadrupeds to autonomously adapt their gait for stable walking under multiple joint faults. Unlike conventional teacher-student approaches that enforce strict imitation, AcL leverages teacher policies to generate style rewards, guiding the student policy without requiring precise replication. We train multiple teacher policies, each corresponding to a different fault condition, and subsequently distill them into a single student policy with an encoder-decoder architecture. While prior works primarily address single-joint faults, AcL enables quadrupeds to walk with up to four faulty joints across one or two legs, autonomously switching between different limping gaits when faults occur. We validate AcL on a real Go2 quadruped robot under single- and double-joint faults, demonstrating fault-tolerant, stable walking, smooth gait transitions between normal and lamb gaits, and robustness against external disturbances.