论文标题
Riemannian几何形状是机器人运动学习和控制的统一理论
Riemannian geometry as a unifying theory for robot motion learning and control
论文作者
论文摘要
Riemannian几何形状是一个数学领域，它一直是革命科学发现的基石，例如一般相对论。尽管在机器人设计和最新应用中使用了具有特定几何形状的数据，但它在机器人技术中的大部分仍被忽略。使用这张蓝天纸，我们认为Riemannian的几何形状为具有许多自由度的机器人提供了最合适的工具来分析和生成良好的机器人运动。通过初步解决方案和新的研究方向，我们讨论了如何利用Riemannian几何形状来设计和结合机器人技术的物理性协同，以及该理论如何为与感知输入的运动协同耦合运动协同效应。
Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in robot design and recent applications for exploiting data with specific geometries, it mostly remains overlooked in robotics. With this blue sky paper, we argue that Riemannian geometry provides the most suitable tools to analyze and generate well-coordinated, energy-efficient motions of robots with many degrees of freedom. Via preliminary solutions and novel research directions, we discuss how Riemannian geometry may be leveraged to design and combine physically-meaningful synergies for robotics, and how this theory also opens the door to coupling motion synergies with perceptual inputs.
