Movement Primitives in Robotics: A Comprehensive Survey

2025-12-17

arXiv.org

AI Summary

This paper presents a systematic review of movement primitive approaches in robot control, with a focus on learning from human demonstrations to generate complex action sequences. Integrating chronological and systematic perspectives, it comprehensively traces the theoretical evolution of movement primitives, key technical advances—including spring-damper modeling, probabilistic coupling of multiple demonstration trajectories, and neural network applications in high-dimensional systems—and their empirical effectiveness in tasks such as grasping and throwing. The study offers an in-depth comparative analysis of prevailing frameworks, establishes for the first time a structured developmental trajectory of the field, and clearly identifies current open challenges and practical limitations, thereby providing both theoretical guidance and a practical roadmap for research in robotic motor skill learning.

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.

Problem

• movement primitives
• robotics
• trajectory representation
• motor control
• human demonstration

Innovation

• Movement Primitives
• Robot Learning from Demonstration
• Trajectory Encoding
• Probabilistic Motion Modeling
• Robotic Control