用神经微分方程学习曲面轨迹，让机器人运动更自然。
Learning from Demonstrations over Riemannian Manifolds using Neural ODEs: An Extended Abstract
- 用神经微分方程逼近黎曼流形上的测地线路径
- 相比传统方法计算量更低，可高效生成复杂运动
- 适合需要精确姿态控制的机器人任务
学习从示范（LfD）通常在欧几里得空间中进行，但机器人的状态（如朝向）本质上在弯曲空间中演化。为生成更自然、复杂的运动，本文研究在能同时编码位置与朝向数据的黎曼流形上进行学习。其中，测地线路径可在流形任意两点间提供自然运动轨迹。我们提出通过神经常微分方程（Neural ODEs）数值估算测地线，从而降低现有方法的高计算开销。最终，这些测地线可解码回原始任务空间并在机器人上部署。本简短报告讨论了框架架构，展示了仿真实验的初步结果，包括与其它测地线计算方法的对比，并探讨了未来工作的挑战与前景。
原文摘要 · Abstract (English)
Learning from demonstratins (LfD) is usually performed over Euclidean spaces, while the robot state, e.g. orientation, naturally evolves over curved spaces. Therefore, to ensure natural, complex motion generation, we investigate learning from demonstrations over Riemannian manifolds that are capable of encoding both position and orientation data. Here, geodesic paths provide for natural motion between two arbitrary points within the manifold. We propose to numerically estimate geodesics via neural ordinary differential equations, mitigating large computational overhead of existing approaches. Finally, these geodesics can be decoded back into the original task space before deploying on the robot. In this extended abstract, we discuss the architecture of our framework, provide some initial insights from our simulation experiments, including comparison to other geodesic computation mechanisms, and discuss the challenges and prospects for future work.
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