Modern quadrupeds are skillful in traversing or even sprinting on uneven terrains in a remote uncontrolled environment. However, survival in the wild requires not only maneuverability, but also the ability to handle potential critical hardware failures. How to grant such ability to quadrupeds is rarely investigated. In this paper, we propose a novel methodology to train and test hardware fault-tolerant controllers for quadruped locomotion, both in the simulation and physical world. We adopt the teacher-student reinforcement learning framework to train the controller with close-to-reality joint-locking failure in the simulation, which can be zero-shot transferred to the physical robot without any fine-tuning. Extensive experiments show that our fault-tolerant controller can efficiently lead a quadruped stably when it faces joint failure during locomotion.

翻译：现代四足机器人在远程非受控环境中穿越甚至冲刺于不平坦地形时表现出色。然而，在野外生存不仅需要机动能力，还需要应对潜在的严重硬件故障的能力。如何赋予四足机器人这种能力鲜有研究。本文提出一种新颖的方法，用于训练和测试四足运动中的硬件容错控制器，涵盖仿真和物理世界。我们采用师生强化学习框架，在仿真中利用接近真实的关节锁定故障训练控制器，并可零样本迁移至物理机器人，无需任何微调。大量实验表明，我们的容错控制器能在四足机器人运动遭遇关节故障时，高效引导其稳定前行。