# Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains

arXiv: https://arxiv.org/abs/2607.13319 · HTML: https://mlsanity.com/paper/2607.13319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 34 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 28 of 28)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Preparing for the Unknown: Learning a Universal Policy with Online System Identification](https://mlsanity.com/paper/1702.02453/) (2017)
- [Model Predictive Path Integral Control using Covariance Variable Importance Sampling](https://mlsanity.com/paper/1509.01149/) (2015)
- [Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2005.06800/) (2020)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators](https://mlsanity.com/paper/1906.01728/) (2019)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Data-efficient Domain Randomization with Bayesian Optimization](https://mlsanity.com/paper/2003.02471/) (2020)
- [Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments](https://mlsanity.com/paper/2104.14938/) (2021)
- [Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain](https://mlsanity.com/paper/2102.12667/) (2021)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees](https://mlsanity.com/paper/2102.09027/) (2021)
- [DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control](https://mlsanity.com/paper/2310.09053/) (2023)
- [AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility](https://mlsanity.com/paper/2409.15783/) (2024)
- [A Multi-step Dynamics Modeling Framework For Autonomous Driving In Multiple Environments](https://mlsanity.com/paper/2305.02241/) (2023)
- [Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control](https://mlsanity.com/paper/2305.00676/) (2023)
- [Autonomous Drifting with 3 Minutes of Data via Learned Tire Models](https://mlsanity.com/paper/2306.06330/) (2023)
- [Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine](https://mlsanity.com/paper/2303.08235/) (2023)
- [Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing](https://mlsanity.com/paper/2309.10716/) (2023)
- [Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving](https://mlsanity.com/paper/2310.08674/) (2023)
- [Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute](https://mlsanity.com/paper/2411.17508/) (2024)
- [A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential](https://mlsanity.com/paper/2312.14808/) (2023)
- [Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.16923/) (2025)
- [Online Adaptation of Learned Vehicle Dynamics Model with Meta-Learning Approach](https://mlsanity.com/paper/2409.14950/) (2024)
- [Beyond Constant Parameters: Hyper Prediction Models and HyperMPC](https://mlsanity.com/paper/2508.06181/) (2025)

## Abstract (arXiv)

High-speed off-road autonomy requires precise closed-loop control for a target vehicle while remaining robust across changing terrains. Recent forward kinodynamic (FKD) prediction foundation models suggest a promising path, starting from a generalist model and specializing it to the target platform. However, effective specialization remains challenging, as it often requires substantial real-world data, and models adapted to one setting can still overfit to specific terrains or driving regimes. We present OptCar (Optimized Car), a recipe for bridging the gap from generalist to specialist FKD models that preserves cross-terrain generalization while optimizing performance for a specific vehicle. OptCar introduces a transformer FKD architecture that uses FiLM to condition multi-step predictions on a single dynamics context token summarizing recent state-action history. It then specializes the generalist model using limited real-world data and targeted synthetic rollouts from environment-specific system identification. In closed-loop model predictive control (MPC) experiments across three terrains and an out-of-distribution cart-pulling task, the largest gains appear at 6 m/s, the highest speed evaluated and the regime in which slip dominates tracking error. On vegetation + dirt, the most slip-diverse terrain, OptCar reduces 6 m/s trajectory tracking error by roughly 55% relative to AnyCar fine-tuned on real data alone, and remains the most accurate even when an unseen cart payload changes the dynamics. With 5 minutes of real data per terrain, OptCar is competitive on road with a specialist trained on 30 minutes of road data and outperforms it when the terrain changes.