  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.
