FT-CPG: Learning Central Pattern Generators for Fault-Tolerant Quadruped Locomotion Under Multi-Joint Failures

Quadruped robots used for rescue and exploration are susceptible to various leg failures, where unpredictable joint locking or power loss can pose an immediate risk of falling. Traditional controllers lack fault-tolerant control capabilities in the case of multi-joint concurrent faults, and erroneous controller outputs may lead to robot damage. This paper proposes a model-free reinforcement learning framework based on central pattern generators (CPG) for fault-tolerant control (FT-CPG). The framework uses biomimetic gait generation and section-wise training to address various types of multi-joint concurrent faults. FT-CPG adopts a fault-tolerant CPG module to generate safe gaits, while utilizing neural network-based policies to infer failures and coordinate the rhythmic behaviors of the CPG, ensuring the ability to track velocity commands under fault conditions. Experiments show that FT-CPG is robust in unexpected situations, where a single leg experiences failures across any number of joints, with each joint randomly encountering locking or power loss faults. Furthermore, the proposed framework preserves the robot's omnidirectional mobility. Finally, zero-shot sim-to-real transfer was successfully implemented on the real-world Unitree Go1 robot, effectively addressing various multi-joint leg failures.