  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.
