2D Mass-spring-like model for prediction of a Sponge's behaviour upon robotic interaction

Veronica E. Arriola-Rios, Jeremy Wyatt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Deformable objects abound in nature, and future robots must be able to predict how they are going to behave in order to control them. In this paper we present a method capable of learning to predict the behaviour of deformable objects. We use a mass-spring-like model, which we extended to better suit our purposes, and apply it to the concrete scenario of robotic manipulation of an elastic deformable object. We describe a procedure for automatically calibrating the parameters for the model taking images and forces from a real sponge as ground truth. We use this ground truth to provide error measures that drive an evolutionary process that searches the parameter space of the model. The resulting calibrated model can make good predictions for 200 frames (6.667 seconds of real time video) even when tested with forces being applied in different positions to those trained.

Original languageEnglish
Title of host publicationRes. and Dev. in Intelligent Syst. XXVIII: Incorporating Applications and Innovations in Intel. Sys. XIX - AI 2011, 31st SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel.
Pages195-208
Number of pages14
DOIs
Publication statusPublished - 2011
Event31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2011 - Cambridge, United Kingdom
Duration: 13 Dec 201115 Dec 2011

Conference

Conference31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2011
Country/TerritoryUnited Kingdom
CityCambridge
Period13/12/1115/12/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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