  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 failures during locomotion.
