Cooperative Manipulation for a Mobile Dual-Arm Robot Using Sequences of Dynamic Movement Primitives

In order to extend promising robot applications in human daily lives, robots need to perform dextrous manipulation tasks, particularly for a mobile dual-arm robot. This paper propose a novel control strategy, which consists of a first trial process and a learning phase, to enable a mobile dual-arm robot to complete a grasp-and-place task which can be decomposed into movement sequences, such as reaching, grasping, and cooperative manipulation of a grasped object. Under the guidance of vision system, the robot with physical constraints successfully fulfills the task by tracking trajectories generated by redundancy resolution online using a neural-dynamic optimization. Then a reinforcement learning algorithm called the policy improvement with path integrals for sequences of dynamic movement primitives is applied to learn and adjust the recorded trajectories. Experimental results of the developed mobile dual-arm robot verified that the proposed strategy is able to successfully and optimally complete a grasp-and-place task.