Autonomous off-road driving is challenging as risky actions taken by the robot may lead to catastrophic damage. As such, developing controllers in simulation is often desirable as it provides a safer and more economical alternative. However, accurately modeling robot dynamics is difficult due to the complex robot dynamics and terrain interactions in unstructured environments. Domain randomization addresses this problem by randomizing simulation dynamics parameters, however this approach sacrifices performance for robustness leading to policies that are sub-optimal for any target dynamics. We introduce a novel model-based reinforcement learning approach that aims to balance robustness with adaptability. Our approach trains a System Identification Transformer (SIT) and an Adaptive Dynamics Model (ADM) under a variety of simulated dynamics. The SIT uses attention mechanisms to distill state-transition observations from the target system into a context vector, which provides an abstraction for its target dynamics. Conditioned on this, the ADM probabilistically models the system's dynamics. Online, we use a Risk-Aware Model Predictive Path Integral controller (MPPI) to safely control the robot under its current understanding of the dynamics. We demonstrate in simulation as well as in multiple real-world environments that this approach enables safer behaviors upon initialization and becomes less conservative (i.e. faster) as its understanding of the target system dynamics improves with more observations. In particular, our approach results in an approximately 41% improvement in lap-time over the non-adaptive baseline while remaining safe across different environments.
