# Fault-Aware Robust Control via Adversarial Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.08728 · HTML: https://mlsanity.com/paper/2011.08728/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control](https://mlsanity.com/paper/2111.10005/) (2021)
- [Adversarial joint attacks on legged robots](https://mlsanity.com/paper/2205.10098/) (2022)
- [Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task](https://mlsanity.com/paper/2602.02895/) (2026)
- [Learning Fault-Tolerant Locomotion with Adaptive Gait Timing](https://mlsanity.com/paper/2608.07328/) (2026)
- [WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation](https://mlsanity.com/paper/2603.17301/) (2026)

## Abstract (arXiv)

Robots have limited adaptation ability compared to humans and animals in the case of damage. However, robot damages are prevalent in real-world applications, especially for robots deployed in extreme environments. The fragility of robots greatly limits their widespread application. We propose an adversarial reinforcement learning framework, which significantly increases robot robustness over joint damage cases in both manipulation tasks and locomotion tasks. The agent is trained iteratively under the joint damage cases where it has poor performance. We validate our algorithm on a three-fingered robot hand and a quadruped robot. Our algorithm can be trained only in simulation and directly deployed on a real robot without any fine-tuning. It also demonstrates exceeding success rates over arbitrary joint damage cases.