论文标题
从演示中学习机器人切割：使用udwadia-kalaba方法的非全面DMP
Learning robotic cutting from demonstration: Non-holonomic DMPs using the Udwadia-Kalaba method
论文作者
论文摘要
动态运动原语（DMP）为编码，生成和调整复杂的最终效应轨迹提供了极大的多功能性。 DMP也非常适合从人类演示中学习操纵技巧。但是，DMP的反应性质限制了其用于工具使用和对象操纵任务的适用性，涉及非全面约束，例如切割手术刀切割或导管转向。在这项工作中，我们通过添加一个耦合术语来扩展笛卡尔空间DMP公式，该耦合术语强制执行一组预定义的非独立约束。我们使用udwadia-kalaba方法获得约束强迫项的闭合形式表达式。这种方法提供了一种干净，实用的解决方案，以确保运行时的限制满意度。此外，约束术语的提议的分析形式可实现有效的轨迹优化，但受约束约束。我们通过展示如何从人类示范中学习机器人切割技能来证明这种方法的有用性。
Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of non-holonomic constraints. We obtain the closed-form expression for the constraint forcing term using the Udwadia-Kalaba method. This approach offers a clean and practical solution for guaranteed constraint satisfaction at run-time. Further, the proposed analytical form of the constraint forcing term enables efficient trajectory optimization subject to constraints. We demonstrate the usefulness of this approach by showing how we can learn robotic cutting skills from human demonstration.
