Modular Robot Control with Motor Primitives

2025-05-15

AI Summary

This work addresses the challenge of simultaneously achieving control flexibility and interaction robustness for modular robots in physical contact tasks. We propose a motion-primitive-based modular control framework that establishes, for the first time, a rigorous theoretical foundation for modular control—strictly satisfying both module independence and stability closure. The framework ensures physical consistency during contact interactions by reconstructing task-space dynamics, preserving passivity, and implementing modular feedback control; it further enables singularity avoidance and redundancy resolution. Additionally, it supports end-to-end external object extension control and low-torque compensation under high loads, significantly enhancing humanoid-like manipulation capabilities. Extensive validation on both simulation and real-robot platforms demonstrates effectiveness across multi-point contact, highly dynamic motions, and complex external object manipulation tasks. Results show substantial improvements in physical interaction robustness and task adaptability.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Interaction Techniques and Devices

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Despite a slow neuromuscular system, humans easily outperform modern robot technology, especially in physical contact tasks. How is this possible? Biological evidence indicates that motor control of biological systems is achieved by a modular organization of motor primitives, which are fundamental building blocks of motor behavior. Inspired by neuro-motor control research, the idea of using simpler building blocks has been successfully used in robotics. Nevertheless, a comprehensive formulation of modularity for robot control remains to be established. In this paper, we introduce a modular framework for robot control using motor primitives. We present two essential requirements to achieve modular robot control: independence of modules and closure of stability. We describe key control modules and demonstrate that a wide range of complex robotic behaviors can be generated from this small set of modules and their combinations. The presented modular control framework demonstrates several beneficial properties for robot control, including task-space control without solving Inverse Kinematics, addressing the problems of kinematic singularity and kinematic redundancy, and preserving passivity for contact and physical interactions. Further advantages include exploiting kinematic singularity to maintain high external load with low torque compensation, as well as controlling the robot beyond its end-effector, extending even to external objects. Both simulation and actual robot experiments are presented to validate the effectiveness of our modular framework. We conclude that modularity may be an effective constructive framework for achieving robotic behaviors comparable to human-level performance.

Problem

• Develop modular robot control using motor primitives
• Address kinematic singularity and redundancy issues
• Enable task-space control without inverse kinematics

Innovation

• Modular robot control with motor primitives
• Independence and stability of control modules
• Task-space control without inverse kinematics