Shape Control of Elastic Objects Based on Implicit Sensorimotor Models and Data-Driven Geometric Features

Conference paper (2022)

Authors

Wanyu Ma Ma The Hong Kong Polytechnic University
J. Zhu Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering , Honda
David Navarro-Alarcon Navarro-Alarcon The Hong Kong Polytechnic University
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

This paper proposes a general approach to design automatic controls to manipulate elastic objects into desired shapes. The object’s geometric model is defined as the shape feature based on the specific task to globally describe the deformation. Raw visual feedback data is processed using classic regression methods to identify parameters of data-driven geometric models in real-time. Our proposed method is able to analytically compute a pose-shape Jacobian matrix based on implicit functions. This model is then used to derive a shape servoing controller. To validate the proposed method, we report a detailed experimental study with robotic manipulators deforming an elastic rod.