发表机构
The University of Texas at Austin; DEVCOM Army Research Laboratory（德克萨斯大学奥斯汀分校; 陆军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究高速越野自主中通用车辆模型适应不同地形的问题，提出OptCar方法，通过历史条件动态适应模块，利用有限真实数据和合成展开微调通用模型，在多种实验中表现良好，提升了跨地形性能。
AI 中文摘要
高速越野自主需要对目标车辆进行精确的闭环控制，同时在不断变化的地形中保持鲁棒性。近期的前向运动动力学（FKD）预测基础模型提供了一条有前景的路径，从通用模型开始并将其专门化到目标平台。然而，有效的专门化仍然具有挑战性。我们提出了OptCar，一种从通用FKD模型到专门FKD模型的桥梁方法，在优化特定车辆性能的同时保留跨地形泛化能力。OptCar引入了一个历史条件动态适应模块，使用有限的真实世界数据和来自特定环境系统识别的目标合成展开对通用模型进行微调。在跨三种地形的闭环模型预测控制（MPC）实验和一个分布外的推车任务中，最大增益出现在6m/s，这是评估的最高速度且是滑移主导跟踪误差的状态。在植被和 dirt 这种最具滑移多样性的地形上，OptCar相对于微调后的AnyCar基线将6m/s轨迹跟踪误差降低了约55%，并且即使在未见过的推车负载改变动力学时仍保持最准确。在每种地形仅使用5分钟真实数据的情况下，OptCar在道路上与使用30分钟道路数据训练的专门模型具有竞争力，一旦地形改变则大幅优于它。
英文摘要
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.
Commentshttps://amrl.cs.utexas.edu/optcar/
