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

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Kartoun Uri -
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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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