Typical robot teaching performed by operators in industrial robot applications increases the operational cost and reduces the manufacturing efficiency. In this paper, an “adult” robot enabled learning method is proposed to solve such teaching problem. This method uses an “adult” robot with advanced sensing and decision-making capabilities to teach “child” robots in manufacturing automation. A Markov Decision Process (MDP) which aims to correct the “child” robot's tool position is formulated and solved using Q-Learning. The proposed algorithm was tested using a mobile robot platform with an in-hand camera (adult) to teach an industrial robot (child) to perform a high accuracy peg-in-hole process. The experimental results demonstrate very robust and stable performance. Because the calibration between the “adult” and “child” robots is eliminated, the flexibility of the proposed method is greatly increased. Hence it can be easily applied in industrial applications where a robot with limited sensing capabilities is installed.

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