The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Online ISSN : 2424-3124
2022
Session ID : 2A1-O05
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Dynamic Cloth Manipulation Considering Variable Stiffness and Cloth Material Change Using Deep Predictive Model Learning
*Kento KAWAHARAZUKAAkihiro MIKIMasahiro BANDOKei OKADAMasayuki INABA
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Abstract

Dynamic manipulation of flexible objects such as cloth, which is difficult to modelize, is one of the major challenges in robotics. Humans can move their arms at high speed using their flexible bodies skillfully, and even when the material to be manipulated changes, they can manipulate the material after moving it several times and understanding its dynamics. Therefore, in this study, we focus on the following two points: (1) body control using a variable stiffness mechanism for dynamic manipulation, and (2) response to changes in the material of the manipulated object using parametric bias. By incorporating these two approaches into a deep prediction model, we show through experiments that a musculoskeletal humanoid can dynamically manipulate cloth while detecting changes in the physical properties of the manipulated object.

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© 2022 The Japan Society of Mechanical Engineers
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