Movement Primitives in Robotics: A Comprehensive Survey
Nolan B. Gutierrez, William J. Beksi
TL;DR
This survey comprehensively catalogs movement primitive frameworks for robotics, tracing the lineage from Dynamic Movement Primitives to ProMPs, KMPs, CNMPs, and Fourier variants. It analyzes each family’s core mechanisms, improvements, limitations, and practical applications across domains such as assembly, HRI, field robotics, humanoid control, and robotic prosthetics. The authors articulate open questions on learning efficiency, generalization, robustness, and industrial deployment, and propose guidelines plus open-source resources to aid practitioners. The work highlights how MPs enable data-efficient, adaptable, and human-centric robotics, while also outlining the real-world challenges that must be overcome for widespread adoption. Overall, MPs remain a central tool for learning-from-demonstration in robotics, offering modular, scalable approaches to complex, uncertain tasks with broad applicability and active research directions.
Abstract
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.