Kinematic Modularity of Elementary Dynamic Actions

TL;DR

This paper introduces a kinematically modular control framework for robots using Elementary Dynamic Actions, enabling diverse movement generation, robustness in contact-rich tasks, and learning through imitation, demonstrated on a KUKA robot.

Contribution

The paper presents a novel modular control approach that simplifies movement generation and handles inverse kinematics issues, with real robot validation.

Findings

Effective movement generation through modular combination

Robustness against contact and physical interaction

Successful implementation on a KUKA robot for various tasks

Abstract

In this paper, a kinematically modular approach to robot control is presented. The method involves structures called Elementary Dynamic Actions and a network model combining these elements. With this control framework, a rich repertoire of movements can be generated by combination of basic modules. The problems of solving inverse kinematics, managing kinematic singularity and kinematic redundancy are avoided. The modular approach is robust against contact and physical interaction, which makes it particularly effective for contact-rich manipulation. Each kinematic module can be learned by Imitation Learning, thereby resulting in a modular learning strategy for robot control. The theoretical foundations and their real robot implementation are presented. Using a KUKA LBR iiwa14 robot, three tasks were considered: (1) generating a sequence of discrete movements, (2) generating a combination of discrete and rhythmic movements, and (3) a drawing and erasing task. The results obtained indicate that this modular approach has the potential to simplify the generation of a diverse range of robot actions.