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.

翻译：几何力学为理解生物与机器人系统如何通过形状变化与环境进行机械交互从而实现运动提供了重要见解。在高摩擦环境中，该理论表明整个交互过程可由"运动性映射"完全刻画。本文比较了从物理机器人运动追踪数据中学习该映射的方法，该机器人专为测试这些方法而设计，具有欠驱动自由度且与基底存在难以建模的交互作用。我们评估了四种建模方法在相同步态内、跨步态及跨速度条件下根据形状变化预测机体速度的能力。结果表明：在小型训练数据集上，简单方法表现更优；而当训练数据更充足时，更复杂的方法则展现出优势。