用物理机器人数据学习低动力系统与环境互动，提升运动预测效率。
Sample Efficient Learning of Body-Environment Interaction of an Under-Actuated System
- 基于运动追踪数据学习机器人的运动映射模型
- 小样本下简单方法更优，大样本下复杂方法表现更好
- 适合对机器人运动建模和数据高效学习感兴趣的读者
几何力学为生物与机器人系统通过形变与环境相互作用实现运动提供了重要洞见。在高摩擦环境中，系统的全部交互可由‘运动映射’（motility map）刻画。本文通过一个专为测试而设计的欠驱动机器人，比较了四种建模方法在从运动追踪数据中学习运动映射的能力，评估其在相同步态内、跨步态及跨速度条件下预测体速的表现。结果表明：在小样本训练时，简单方法更优；而在更多训练数据下，复杂方法更具优势，揭示了模型复杂度与数据量之间的权衡。
原文摘要 · Abstract (English)
Geometric mechanics provides valuable insights into how biological and robotic systems use changes in shape to move by mechanically interacting with their environment. In high-friction environments it provides that the entire interaction is captured by the ``motility map''. Here we compare methods for learning the motility map from motion tracking data of a physical robot created specifically to test these methods by having under-actuated degrees of freedom and a hard to model interaction with its substrate. We compared four modeling approaches in terms of their ability to predict body velocity from shape change within the same gait, across gaits, and across speeds. Our results show a trade-off between simpler methods which are superior on small training datasets, and more sophisticated methods, which are superior when more training data is available.
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