Cooperative Human-Robot Learning System using a Virtual Reality Telerobotic Interface

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A cooperative human-robot learning system for remote robotic operations using a virtual reality (VR) interface is presented. The paper describes the overall system architecture, and the VR telerobotic system interface. Initial tests using on-line control through the VR interface for the task of shaking out the contents of a plastic bag are presented. The system employs several state-action policies. System states are defined by: type of bag, status of robot, and environment. Actions are defined by initial grasping point, lift and shake trajectory. A policy is a set of state-action pairs to perform a robotic task. The system starts with knowledge of the individual operators of the robot arm, such as opening and closing the gripper, but it has no policy for deciding when these operators are not appropriate, nor does it have knowledge about the special properties of the bags. An optimal policy is defined as the best action for a given state that is learned from experience and human guidance. A policy is found to be beneficial if a bag was grabbed successfully and all its contents extracted.
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