Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex nonlinear relationships between tasks. In this article, we develop a novel policy synthesis algorithm, Riemannian motion policy (RMP)flow, based on geometrically consistent transformations of RMPs. RMPs are a class of reactive motion policies that parameterize non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task spaces. Given a set of RMPs designed for individual tasks, RMPflow can combine these policies to generate an expressive global policy, while simultaneously exploiting sparse structure for computational efficiency. We study the geometric properties of RMPflow and provide sufficient conditions for stability. Finally, we experimentally demonstrate that accounting for the natural Riemannian geometry of task policies can simplify classically difficult problems, such as planning through the clutter on high-degree-of-freedom manipulation systems. Note to Practitioners —Requirements on safety and responsiveness for collaborative robots have driven a need for new ideas in control design that bridge between standard objectives in low-level control (such as trajectory tracking) and high-level behavioral objectives (such as collision avoidance) often relegated to planning systems. Modern results from geometric control, which promise stable controllers that can smoothly and safely transition between many behavioral tasks, therefore, become highly relevant. However, for years, this field has remained inaccessible due to its mathematical complexity. This article aims to: 1) make those ideas accessible to robotics and control experts by recasting them in a concrete algorithmic framework amenable to controller design and 2) to additionally generalize them to better satisfy the specific needs of robotic behavior generation. Our experiments demonstrate that the resulting controllers can engender natural behavior that adapts instantaneously to changing surroundings with zero planning while performing manipulation tasks. The framework is gaining traction within the robotics community, finding increasing application in areas, such as autonomous navigation, tactile servoing, and multi-agent systems. Future research will address learning these controllers from data to simplify that process of design and tuning, which at present can require experience.

中文翻译：

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制冷剂管理计划流：生成多任务运动策略的几何框架

在动态环境中为多个任务生成机器人运动具有挑战性，需要一种算法来做出反应，同时考虑到任务之间复杂的非线性关系。在本文中，我们基于 RMP 的几何一致变换开发了一种新颖的策略合成算法，黎曼运动策略 (RMP) 流。RMP 是一类反应性运动策略，它将非欧几里得行为参数化为本质非线性任务空间中的动态系统。给定一组为单个任务设计的 RMP，RMPflow 可以结合这些策略来生成具有表现力的全局策略，同时利用稀疏结构来提高计算效率。我们研究了 RMPflow 的几何特性，并为稳定性提供了充分条件。最后，从业者须知 — 对协作机器人的安全性和响应性的要求推动了控制设计中对新思想的需求，这些思想在低级控制的标准目标（如轨迹跟踪）和高级行为目标（如避免碰撞）之间建立了桥梁，这些目标通常被归为规划系统。几何控制的现代结果保证了稳定的控制器可以在许多行为任务之间平稳安全地转换，因此变得高度相关。然而，多年来，由于其数学复杂性，该领域一直无法进入。本文旨在：1）通过在适合控制器设计的具体算法框架中重铸这些想法，使机器人和控制专家可以使用这些想法；2）另外概括它们以更好地满足机器人行为生成的特定需求。我们的实验表明，由此产生的控制器可以产生自然行为，在执行操作任务时，以零规划立即适应不断变化的环境。该框架在机器人社区中越来越受欢迎，在自主导航、触觉伺服和多代理系统等领域得到越来越多的应用。未来的研究将解决从数据中学习这些控制器的问题，以简化目前需要经验的设计和调整过程。我们的实验表明，由此产生的控制器可以产生自然行为，在执行操作任务时，以零规划立即适应不断变化的环境。该框架在机器人社区中越来越受欢迎，在自主导航、触觉伺服和多代理系统等领域得到越来越多的应用。未来的研究将解决从数据中学习这些控制器的问题，以简化目前需要经验的设计和调整过程。我们的实验表明，由此产生的控制器可以产生自然行为，在执行操作任务时，以零规划立即适应不断变化的环境。该框架在机器人社区中越来越受欢迎，在自主导航、触觉伺服和多代理系统等领域得到越来越多的应用。未来的研究将解决从数据中学习这些控制器的问题，以简化目前需要经验的设计和调整过程。