Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network

Abstract

For dynamic manipulation of flexible objects, we propose an acquisition method of a flexible object motion equation model using a deep neural network and a control method to realize a target state by calculating an optimized time-series joint torque command. By using the proposed method, any physics model of a target object is not needed, and the object can be controlled as intended. We applied this method to manipulations of a rigid object, a flexible object with and without environmental contact, and a cloth, and verified its effectiveness.

References in corpus (2)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

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