发表机构
George Mason University; University of Catania（乔治梅森大学; 卡塔尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出VertiAKD框架，可在几何与语义复杂地形上跨不同车辆迁移适配越野运动动力学知识，仅需一分钟新数据即可显著降低长 horizon 预测误差，实现鲁棒闭环轨迹跟踪。
AI 中文摘要
越野移动性要求自主移动机器人能够在异构车队和持续变化的地形条件下实现泛化。现有的跨车辆适应方法通常假设地形平坦，而感知地形的运动动力学模型往往需要针对特定平台进行数据采集和重新训练。为此，我们提出VertiAKD，这是一个可在几何和语义复杂地形上同时实现不同车辆间越野运动动力学知识迁移与适配的统一框架。VertiAKD学习一个共享的移动性表示，该表示联合编码车辆配置、轨迹转换以及局部高程和语义地形特征。在新车辆在未见过的地形上运行且数据有限的情况下，VertiAKD会识别最相关的移动性描述符，并通过函数编码器迁移其知识以初始化感知地形的运动动力学模型，随后该模型可基于流式观测进行周期性在线优化，无需基于梯度的重新训练。我们在基于Chrono多物理引擎构建的Verti-Bench模拟器以及Verti-4-Wheeler平台的五种物理配置上对VertiAKD进行了评估。仅需一分钟的新轨迹数据及相关地形特征，VertiAKD相较于跨不同未见过车辆配置的直接移动性描述符迁移方法，可将长 horizon 预测误差降低多达34.52%，相较于对比基线方法则降低94.43%。我们进一步在仿真和物理实验中验证了其鲁棒的闭环轨迹跟踪能力，凸显了感知地形的跨车辆知识迁移在精确建模和可靠越野导航方面的有效性。
英文摘要
Off-road mobility requires autonomous mobile robots to generalize across heterogeneous vehicle fleets and continuously changing terrain conditions. Existing cross-vehicle adaptation approaches generally assume flat terrain, while terrain-aware kinodynamic models often require platform-specific data collection and retraining. To this end, we propose VertiAKD, a unified framework for transferring and adapting off-road kinodynamic knowledge across diverse vehicles on geometrically and semantically complex terrain simultaneously. VertiAKD learns a shared mobility representation that jointly encodes vehicle configurations, trajectory transitions, and local elevation and semantic terrain features. Given limited data from a novel vehicle operating on unseen terrain, VertiAKD identifies the most relevant mobility descriptors and transfers their knowledge to initialize a terrain-aware kinodynamic model via function encoders, which is then periodically refined online from streaming observations without gradient-based retraining. We evaluate VertiAKD in the Verti-Bench simulator, built on the Chrono multi-physics engine, and on five physical configurations of the Verti-4-Wheeler platform. With only one minute of new trajectory data and associated terrain features, VertiAKD reduces long-horizon prediction error by up to 34.52% over direct mobility descriptor transfer across diverse unseen vehicle configurations and 94.43% over competing baselines. We further demonstrate robust closed-loop trajectory tracking in both simulation and physical experiments, highlighting the effectiveness of terrain-aware cross-vehicle knowledge transfer for accurate modeling and reliable off-road navigation.
