Modular Robot Control with Motor Primitives

TL;DR

This paper introduces a modular control framework for robots based on motor primitives, enabling complex behaviors, task-space control, and physical interaction handling, inspired by biological motor control and validated through simulations and real robot experiments.

Contribution

It formulates a comprehensive modular control approach for robots using motor primitives, addressing stability, independence, and enabling advanced behaviors without inverse kinematics.

Findings

Robust task-space control without inverse kinematics.

Handling of kinematic singularities and redundancies.

Successful validation through simulations and real robot tests.

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.