Satoshi Funabashi

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Moving objects within the hand is challenging, especially if the objects are of various shape and size. In this paper we use machine learning to learn in-hand manipulation of such various sized and shaped objects. The TWENDY-ONE hand is used, which has various properties that makes it well suited for in-hand manipulation: a high number of actuated joints,(More)
This study aims to develop a fingertip in order to improve the manipulation performance of posture interpolation control for a multifingered robot hand. Increasing the contact area between the fingertips and object is one solution for improving grasping and manipulation stability. A reasonable fingertip shape design and material selection can increase the(More)
In-hand manipulation is often needed to accomplish a practical task after grasping an object. In-hand manipulation of variously sized and shaped objects in multi-fingered hands without dropping the object is challenging. In this paper we suggest a combined strategy of force control and passive adaptation through soft fingertips with simple interpolation(More)
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