Beyond webcams
Task Learning Based on Reinforcement Learning in Virtual Environment
Neural Information Processing
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Since general automatic assembly motion planning using a geometric CAD model is computationally difficult, automation of a general assembly task is not feasible. However, if a human operator roughly specifies an assembly motion, the remainder of the process except for planning can be automated. We construct a teaching system for assembly tasks according to the above concept. This paper describes our teaching system for assembly tasks by using a position/force simulator. We show how to construct the position/force simulator and analyse the hybrid position/force control which is needed when dealing with general rotational motion. We show the example of extracting a sequence of contact state transitions from the motion which the operator performs in the position simulator of our system. The sequence of contact states automatically extracted from the motion shown by the operator makes it feasible to achieve an error-tolerant automated assembly motion. If a sequence of contact states is obtained, studies of the automatic assembly task system can progress based upon this information.