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.

翻译：本文提出了一种面向机器人控制的运动学模块化方法，该方法构建了名为"基础动力学动作"的结构单元以及组合这些单元的网状模型。在该控制框架下，通过基础模块的组合即可生成丰富的动作库，同时规避了逆运动学求解、运动学奇异点处理及运动学冗余控制等难题。该模块化方法对接触与物理交互具有鲁棒性，尤其适用于接触密集的操控场景。每个运动学模块可通过模仿学习获取，由此形成机器人控制的模块化学习策略。本文阐述了该方法的理论基础及其在真实机器人上的实现过程。采用KUKA LBR iiwa14机器人完成了三项任务：(1)生成离散动作序列，(2)生成离散动作与节律动作的组合序列，(3)绘画与擦除任务。实验结果表明，该模块化方法具有简化多样化机器人动作生成的潜力。