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.
翻译:机器人与人体和动物相比在损坏情况下的适应能力有限。 然而,机器人损害在现实应用中非常普遍, 特别是对于部署在极端环境中的机器人。 机器人的脆弱性极大地限制了其广泛应用。 我们提议了一个对抗性强化学习框架, 大幅提高机器人在操作任务和移动任务中共同损害案件中的稳健性。 该代理人在联合损害案件中的性能差的情况下接受迭接培训。 我们验证了我们的算法, 我们使用的是三指机器人手和四分立机器人。 我们的算法只能进行模拟培训, 并且直接在不作任何微调的机器人上部署。 它还表明在任意联合损害案件中的成功率极高。