Satoshi Funabashi

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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)
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)
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