系统梳理机器人运动基元方法,助你快速掌握运动规划核心框架
Movement Primitives in Robotics: A Comprehensive Survey
- 按时间脉络梳理运动基元的各类建模框架与实现思路
- 涵盖从弹簧阻尼系统到神经网络的多种可解析运动表示方法
- 适合机器人控制、模仿学习研究者快速了解技术全貌
生物系统展现出连续的运动序列,由一系列动作片段构成,使它们能以创造性和灵活性完成复杂任务。这一观察促使研究人员寻找运动的基本构建块——运动基元,这些基元在自主系统(如机器人)中特别适合生成运动指令。本文提供了一部关于运动基元方法及其应用的编年史式全面综述。具体而言,我们介绍运动基元框架作为通过人类示范获取的机器人控制轨迹的表示方式。在机器人领域,运动基元可编码轨迹级别的基本动作,例如机器人抓取杯子或投掷球所需的运动序列。此外,运动基元还被设计具备弹簧-阻尼系统的解析特性、多示范的概率耦合能力、在高维系统中使用神经网络等优势,以应对机器人中的诸多挑战。尽管运动基元已广泛应用于多个领域,本文旨在为从业者提供其在机器人中的应用指导,具体包括:(i) 系统回顾主要运动基元框架并分析其优缺点;(ii) 展示成功应用运动基元的实际案例;(iii) 探讨当前开放问题及实际应用中的挑战。
原文摘要 · Abstract (English)
Biological systems exhibit a continuous stream of movements, consisting of sequential segments, that allow them to perform complex tasks in a creative and versatile fashion. This observation has led researchers towards identifying elementary building blocks of motion known as movement primitives, which are well-suited for generating motor commands in autonomous systems, such as robots. In this survey, we provide an encyclopedic overview of movement primitive approaches and applications in chronological order. Concretely, we present movement primitive frameworks as a way of representing robotic control trajectories acquired through human demonstrations. Within the area of robotics, movement primitives can encode basic motions at the trajectory level, such as how a robot would grasp a cup or the sequence of motions necessary to toss a ball. Furthermore, movement primitives have been developed with the desirable analytical properties of a spring-damper system, probabilistic coupling of multiple demonstrations, using neural networks in high-dimensional systems, and more, to address difficult challenges in robotics. Although movement primitives have widespread application to a variety of fields, the goal of this survey is to inform practitioners on the use of these frameworks in the context of robotics. Specifically, we aim to (i) present a systematic review of major movement primitive frameworks and examine their strengths and weaknesses; (ii) highlight applications that have successfully made use of movement primitives; and (iii) examine open questions and discuss practical challenges when applying movement primitives in robotics.
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