Advancing coordination control of multi-limb robotic tasks: application to bimanual dynamic manipulation and bipedal reactive locomotion

Bi-manual picking up of objects to toss them on a conveyor belt are dynamic manipulation activities generated daily in the industry. Such repetitive and physically demanding tasks are still done largely by humans for lack of similarly fast, precise, and robust bi-manual robotic systems. With nowadays booming of e-commerce, the needs for faster package handling solutions continue to increase. Hence, automation and robotics is the only viable solution as the current workforce cannot keep up with the growing industry demands. In current applications, however, robots usually use quasi-static approaches (with near zero relative velocities) to grab and release objects, mainly to avoid impacts. Thus, this thesis aims to develop dynamic alternative solutions to quasi-static manipulation approaches with the goal to accelerate object handling operations and improve their energy efficiency. It focuses on bi-manual (dual-arm) robotic manipulation for its potential to mimic humans' dexterity. To achieve its goal, the thesis considers different scenarios with two types of bi-manual systems: a biped humanoid robot and a pair of fixed base robot manipulators. The first part of the thesis starts by endowing the humanoid robot with balance and locomotion abilities needed for dual-arm cooperative tasks. It proposes a reactive locomotion controller that exploits the capture point dynamics to generate on-the-fly adjustable omnidirectional walking patterns that are consistent with balance constraints. In the second part, the whole body of the humanoid robot is controlled to accomplish bi-manual motion coordination and cooperative compliant manipulation tasks. The proposed approach relies on dynamical systems and exploits a shrinkable virtual object concept with its associated constraints to achieve robust coordination of the robotic hands with smooth transitions between non-contact and contact motion phases. The framework uses quadratic programming (QP) to generate constraint-consistent interaction wrenches that stabilize the grasp and perform the desired manipulation tasks. The third part of the thesis considers more dynamic interactions of the dual-arm system with the objects by allowing the grabbing and releasing of objects with non-zero relative velocities. Such an approach, besides speeding up the task, offers the possibility to expand the robot's reach beyond its physical boundaries. Thus, a unified coordination framework based on modulated dynamical systems is proposed for reaching, grabbing with impact, and tossing objects in one swipe. It is based on modulated dynamical systems that ensure motion continuity and robustness throughout the task. The last part of the thesis extends the dynamic framework by enabling the precise tossing of objects onto a moving target carried by a conveyor belt. It uses a learned inverse throwing map within a kinematic-based bi-level optimization to determine feasible tossing parameters required by the task. Moreover, it proposes and uses a model of the tossable workspace to determine intercept locations that yield a high probability of success. Finally, the proposed approaches are validated both in simulation and on real robotic platforms: a humanoid robot iCub and a pair of KUKA LBR (IIWA7 and IIWA14) robots. Kinetic comparisons with the classical pick-and-place strategy are conducted and the results show that the proposed swift pick-and-toss reduces the task duration and the energy expenditure.