抄録
This paper describes learning of robust haptic recognition by Bionic Hand, a human-like robot hand, through dynamic interaction with the object. Bionic hand is a human-like hand that has soft skin with distributed receptors and is driven by artificial pneumatic muscles. At the beginning of learning, it utilizes the result of physical interaction with the object: thanks to the hand compliance, regrasping will leads object's posture to stable one in the hand. This result can be successively used as object classification for learning dynamic interaction beetween the hand and the object by a recurrent neural network. We conduct experiments and show that the proposed method is effective for robust and fast object recognition.
