AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control

-- Quadrupedal robots can learn versatile locomotion skills but remain vulnerable when one or more joints lose power . In contrast, dogs and cats can adopt limping gaits when injured, demonstrating their remarkable ability to adapt to physical conditions. Inspired by such adaptability, this paper presents Action Learner (AcL), a novel teacher-student reinforcement learning framework that enables quadrupeds to autonomously adapt their gait for stable walking under multiple joint faults. Unlike conventional teacher-student approaches that enforce strict imitation, AcL leverages teacher policies to generate style rewards, guiding the student policy without requiring precise replication. We train multiple teacher policies, each corresponding to a different fault condition, and subsequently distill them into a single student policy with an encoder-decoder architecture. While prior works primarily address single-joint faults, AcL enables quadrupeds to walk with up to four faulty joints across one or two legs, autonomously switching between different limping gaits when faults occur . Quadruped robots are gaining popularity as versatile mobile platforms capable of navigating diverse terrains and performing robust locomotion tasks such as search and rescue operations in buildings, cargo delivery in cities, and planetary exploration. In such scenarios, quadrupeds may encounter faults that cannot be immediately repaired, requiring them to continue their tasks despite the malfunction.