实时适应复杂地形的动态建模，提升机器人导航可靠性
Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments
- 用函数编码器学习通用动态基函数，实现快速在线更新
- 仅需少量实时数据即可完成适应，无需重新训练
- 适合在未知或变化地形中运行的自主移动机器人
在偏远、非结构化环境中运行的自主移动机器人必须应对快速变化的不可预测地形。在此类场景中，准确估计机器人在变化地形上的动态特性是实现可靠、精确导航与规划的关键挑战。本文提出一种基于函数编码器的地形感知动态建模与规划的在线自适应方法。该方法利用有限的在线数据，在运行时高效适应新地形，无需重新训练或微调。通过学习一组覆盖多种地形上机器人动态的神经网络基函数，可将新未见地形的适应简化为一次最小二乘计算。我们在Unity机器人仿真环境中验证该方法，结果表明，下游控制器因模型精度更高而表现更优：在杂乱环境中导航时碰撞次数显著少于神经ODE基线方法。
原文摘要 · Abstract (English)
Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a critical challenge becomes estimating the robot's dynamics on changing terrain in order to enable reliable, accurate navigation and planning. We present a novel online adaptation approach for terrain-aware dynamics modeling and planning using function encoders. Our approach efficiently adapts to new terrains at runtime using limited online data without retraining or fine-tuning. By learning a set of neural network basis functions that span the robot dynamics on diverse terrains, we enable rapid online adaptation to new, unseen terrains and environments as a simple least-squares calculation. We demonstrate our approach for terrain adaptation in a Unity-based robotics simulator and show that the downstream controller has better empirical performance due to higher accuracy of the learned model. This leads to fewer collisions with obstacles while navigating in cluttered environments as compared to a neural ODE baseline.
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