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Model-free vision-based shaping of deformable plastic materials

journal contribution
posted on 2022-09-29, 06:13 authored by Andrea Cherubini, Valerio Ortenzi, Akan CosgunAkan Cosgun, Robert Lee, Peter Corke
We address the problem of shaping deformable plastic materials using non-prehensile actions. Shaping plastic objects is challenging, because they are difficult to model and to track visually. We study this problem, by using kinetic sand, a plastic toy material that mimics the physical properties of wet sand. Inspired by a pilot study where humans shape kinetic sand, we define two types of actions: pushing the material from the sides and tapping from above. The chosen actions are executed with a robotic arm using image-based visual servoing. From the current and desired view of the material, we define states based on visual features such as the outer contour shape and the pixel luminosity values. These are mapped to actions, which are repeated iteratively to reduce the image error until convergence is reached. For pushing, we propose three methods for mapping the visual state to an action. These include heuristic methods and a neural network, trained from human actions. We show that it is possible to obtain simple shapes with the kinetic sand, without explicitly modeling the material. Our approach is limited in the types of shapes it can achieve. A richer set of action types and multi-step reasoning is needed to achieve more sophisticated shapes.

History

Journal

The International Journal of Robotics Research

Volume

39

Issue

14

Pagination

1739 - 1759

Publisher

SAGE Publications

ISSN

0278-3649

eISSN

1741-3176

Language

en

Publication classification

C1 Refereed article in a scholarly journal

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