Date: Tuesday, July 28, 2026 Time: 1:30pm- 2:30pm Location: GHC 9115 Title: Terrain-Aware Dynamics Models for High-Speed Off-Road Navigation Abstract: High-speed autonomy in the real world requires accurate control, which often relies on dynamics models that capture the complex interaction between a robot and its environment. In off-road regimes, this terrain interaction dominates the robot's dynamics, driven by formidable characteristics such as diverse surface properties, complex geometries, environment diversity, and high-speed instability. This thesis investigates how terrain-aware perception can improve learned dynamics modeling and control at high speed and how such models can be rigorously evaluated before deployment. First, we demonstrate how perception representations can make dynamics models terrain-aware, capturing geometric and semantic details that physics-based models miss and that simplistic learned models overlook. We formulate a representation that extracts the most relevant terrain features given a robot's motion, yielding higher prediction accuracy. Second, we introduce a rigorous evaluation method to mitigate real-world failures. We collect a challenging, multi-season dataset at speeds up to 13 m/s and mine the most difficult evaluation samples using our benchmarking method. While models appear accurate on average, our benchmark surfaces the long-tail failure cases where prior models fail catastrophically. Together, with our verified, terrain-aware model, we decrease the worst-case prediction error by 23.8%, compared to physics-based and learned baselines. We further evaluate on a full-scale ATV platform across high-speed (>10m/s) and geometrically challenging courses with a 34.9% reduction in maximum cross-track error. These results demonstrate the importance of embedding environment context for locomotion-related tasks. Committee: Prof. Wenshan Wang (chair) Prof. Sebastian Scherer (chair) Prof. Aaron Johnson Anoushka Alavilli