RINA: Rapid Introspective Neural Adaptation for Out-of-Distribution Payload Configurations on Quadruped Robots

Abstract

Adaptive locomotion is a fundamental capability for quadruped robots, particularly in real-world scenarios when they must transport novel or out-of-distribution (O.O.D.) payloads across diverse terrains. Previous learning-based methods often tightly couple a locomotion controller's learned parameters with the adaptation process, which requires extensive pre-training or slow online updates when encountering O.O.D. payloads. To enable adaptation of quadruped locomotion to O.O.D. payloads, we propose the novel Rapid Introspective Neural Adaptation (RINA) method that rapidly compensates for differences between expected and actual joint torques caused by O.O.D. payloads. RINA introduces an adaptive residual dynamics representation that decouples the learning model's parameters from those used for adaptation. A new neural operator network is introduced to learn a set of basis functions as the learning model, which are combined using linear coefficients to predict residual dynamics. Then, these residual dynamics are used to adjust the locomotion controller's output, compensating for additional torques induced by the O.O.D. payload. During execution, the mixing coefficients can be rapidly and introspectively adapted on-the-go to generate joint torque compensations for O.O.D. payloads, while keeping the learned basis functions unchanged. Experimental results have demonstrated that our RINA approach well addresses on-the-go O.O.D. payload adaptation on varied natural terrains without collecting and retraining on additional data and outperforms baseline methods. More details of this work are provided on the project website: https://hcrlab.gitlab.io/project/rina.