Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments

--Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a critical challenge becomes estimating the robot's dynamics on changing terrain in order to enable reliable, accurate navigation and planning. We present a novel online adaptation approach for terrain-aware dynamics modeling and planning using function encoders. By learning a set of neural network basis functions that span the robot dynamics on diverse terrains, we enable rapid online adaptation to new, unseen terrains and environments as a simple least-squares calculation. We demonstrate our approach for terrain adaptation in a Unity-based robotics simulator and show that the downstream controller has better empirical performance due to higher accuracy of the learned model. This leads to fewer collisions with obstacles while navigating in cluttered environments as compared to a neural ODE baseline. Rapid adaptation to unknown environments and terrain is critical for autonomous mobile robots. In off-road navigation, unpredictable terrain features such as rocky paths, forest floors, and wet fields can cause skidding, tripping, or immobilization, jeopardizing the robot's ability to reach its objective. Autonomous ground vehicles must therefore dynamically adjust their behavior to terrain-specific conditions. This adaptation is challenging because terrain variations directly alter system dynamics. For example, tire response to acceleration depends on surface friction.