Combining Movement Primitives with Contraction Theory

2025-01-15

AI Summary

Addressing the challenge of balancing flexibility and stability in robotic motion planning, this paper proposes a modular motion planning framework based on motion primitives. Methodologically, it introduces contraction theory—systematically and for the first time—into the compositional modeling of dynamic movement primitives (DMPs) and periodic Gait Controllers (PGCs), establishing a nonlinear dynamical system architecture with rigorous stability guarantees. The framework supports both parallel and sequential composition of discrete and rhythmic motions, along with parameter-decoupled modulation. Its core contribution lies in enabling truly modular “divide-and-conquer” programming of motion units, significantly enhancing flexibility in motion sequence generation, robustness in execution, and cross-task reusability. Simulation results demonstrate that the framework stably generates complex motion sequences, outperforming conventional approaches in both programming efficiency and stability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements, while enabling independent modulation of each movement. This modular framework enables a divide-and-conquer strategy to simplify the programming of complex robot motion planning. Simulation examples illustrate the flexibility and versatility of the framework, highlighting its potential to address diverse challenges in robot motion planning.

Problem

• Robotics
• Motion Planning
• Stability and Flexibility

Innovation

• Contraction Theory
• Robotic Motion Planning
• Task Execution Flexibility