HAFT: History-Aware Flow Policy for Torque-Degraded Quadrupedal Locomotion

Legged locomotion policies are typically optimized under nominal actuation conditions, implicitly assuming a stable relationship between control commands and the resulting robot dynamics. Actuator degradation violates this assumption by reducing joint-level actuator effectiveness, thereby altering the closed-loop dynamics and compromising coordinated whole-body locomotion. A fundamental challenge is that actuator effectiveness is not directly observable from instantaneous proprioception and must instead be inferred from the robot's temporal responses to its recent actions. Viewing this challenge through the lens of online system identification, we exploit recent observation–action history as informative evidence of the underlying actuation condition. Based on this insight, we propose HAFT, a history-conditioned framework for degradation-adaptive legged locomotion. HAFT infers latent actuator effectiveness from recent interactions and selectively aggregates temporal context according to the current robot state and inferred degradation condition. The resulting context is used to condition a flow-matching policy for joint action generation, enabling the policy to adapt whole-body coordination to changes in available actuation. Extensive experiments on quadrupedal locomotion under diverse actuator degradation patterns and severities demonstrate that HAFT consistently improves locomotion robustness and adaptation. Our code is available at https://anonymous.4open.science/r/HAFT-1B35.