Learning Forward Looking Adaptation to Dynamic Payloads for Quadruped Locomotion Via Physics-Informed Neural Networks

Abstract

Payload-adaptive locomotion is an essential capability for quadruped robots operating in real-world scenarios, particularly when tasked with transporting dynamic payloads. Existing approaches face fundamental limitations: reactive adaptation strategies respond too slowly to sudden payload changes, while learning-based methods often yield physically inconsistent models of robot dynamics that generalize poorly to novel states. To address these key challenges, we introduce Forward-Looking Adaptation to Dynamic Payloads (FLAP), a novel approach that learns to proactively compensate for discrepancies between expected and actual locomotion behavior induced by dynamic payloads. FLAP combines two critical components: (1) a physics-informed neural network (PINN) that predicts anticipated joint states while enforcing physical consistency through dynamics based loss functions, and (2) a composite adaptive control law that rapidly generates anticipatory joint torque compensations based on the PINN’s predictions. Through unifying structured dynamics modeling with real-time anticipatory control, our method enables generalizable and physically consistent adaptation to dynamic payloads. Experimental results demonstrate that FLAP achieves robust locomotion under diverse payload conditions on physical quadruped robots in real-world environments.