Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human–robot movement coordination. It uses imitation learning to construct a mixture model of human–robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human–robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories

Manipulation planning under changing external forces

This paper presents a planner that enables robots to manipulate objects under changing external forces. Particularly, we focus on the scenario where a human applies a sequence of forceful operations, e.g. cutting and drilling, on an object that is held by a robot. The planner produces an efficient manipulation plan by choosing stable grasps on the object, by intelligently deciding when the robot should change its grasp on the object as the external forces change, and by choosing subsequent grasps such that they minimize the number of regrasps required in the long-term. Furthermore, as it switches from one grasp to the other, the planner solves the bimanual regrasping in the air by using an alternating sequence of bimanual and unimanual grasps. We also present a conic formulation to address force uncertainties inherent in human-applied external forces, using which the planner can robustly assess the stability of a grasp configuration without sacrificing planning efficiency. We provide a planner implementation on a dual-arm robot and present a variety of simulated and real human-robot experiments to show the performance of our planner