Fault-Aware Robust Control via Adversarial Reinforcement Learning

• URL: http://arxiv.org/abs/2011.08728v2
• Date: Mon, 30 Nov 2020 06:30:25 GMT
• Title: Fault-Aware Robust Control via Adversarial Reinforcement Learning
• Authors: Fan Yang, Chao Yang, Di Guo, Huaping Liu, Fuchun Sun
• Abstract summary: We propose an adversarial reinforcement learning framework, which significantly increases robot fragility over joint damage cases. 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.
• Score: 35.16413579212691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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