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 implementation on a real robot are presented. Using a KUKA LBR iiwa 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 show that this modular approach has the potential to simplify the generation of a diverse range of robot actions.

A prominent challenge in the field of robotics is manipulation of flexible objects. One major factor that makes this task difficult is the complex dynamics emerging from its high-dimensional structure. This argues against the use of popular optimization-based approaches, which scale poorly with system dimension (the "curse of dimensionality"). Nevertheless, almost indifferent to this complexity, humans handle it on a daily basis, without any apparent difficulty.Inspired by human motor control, we propose that encoding movements based on dynamic primitives can simplify the task of manipulating flexible objects and provides a way around the curse of dimensionality. Using an extreme example — manipulating a whip — we tested in simulation whether targets at various locations could be reached with a whip by using a controller based on dynamic primitives. Regardless of the target location, this approach successfully managed the complexity of a 54 degree-of-freedom system (yielding a 108-dimensional state-space representation) and identified an upper-limb movement that achieved the task. This approach did not require a detailed model of the whip, which thereby significantly simplified the computational complexity of the control task. We believe that this approach may facilitate robotic manipulation of flexible materials, and in general afford a simplified way to control dynamically complex objects.

Manipulation of flexible objects is one of the major challenges in robotics as the nonlinear dynamics of the high-dimensional object structure makes it difficult to apply current control methods. A previous simulation study showed that control with few pre-structured joint trajectories coupled with joint impedance (dynamic primitives) could control a 25-dimensional whip to hit a target. This was possible even though the impedance values were constant. This paper explores whether time-varying impedance throughout the movement may further enhance performance. We present an online impedance adaptation (OIA) controller that modulates the joint impedances of a two-joint actuator in real time for the same task. Results showed that the OIA control method increased the speed of optimization and resulted in smaller deviation from the zero-torque joint trajectories compared to the controller with constant joint impedances. This novel way to modulate both motion and impedance of a manipulator may facilitate the control of flexible objects with significant dynamics.

There are two major challenges in universal stair climbing: stairs without riser and with nose, and stairs with various dimensions. In this study, we proposed an indoor robot platform to overcome these challenges. First, to create an angle of attack, the Tusk, a passive, protruded element, was added in front of a 4-wheel robot. For design analysis and optimization of the Tusk, a simplified model of universal stair climbing was applied. To accommodate stairs without risers and with nose, the assistive track mechanism was applied. To climb the stair regardless of its dimension, length-adjustable mechanism was added. The results indicated the robot with these mechanisms successfully overcame each challenge. The performance was better than most conventional stair-climbing robots in terms of the range of compatible stairs. We expect these new approaches to expand the range of indoor robot operation with minimal cost.