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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.
|Title of host publication||Research and Development in Intelligent Systems XXVIII|
|Subtitle of host publication||Incorporating Applications and Innovations in Intelligent Systems XIX Proceedings of AI-2011, the Thirty-first SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence|
|Editors||Max Bramer, Miltos Petridis, Lars Nolle|
|Number of pages||14|
|Publication status||Published - 17 Nov 2011|
|Event||31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2011 - Cambridge, United Kingdom|
Duration: 13 Dec 2011 → 15 Dec 2011
|Conference||31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2011|
|Period||13/12/11 → 15/12/11|
- Ground Truth
- Force Sensor
- Finite Element Method Model
- Graphical Constraint
- Deformable Object
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
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- 1 Finished
Wyatt, J., Dearden, R. & Sloman, A.
1/05/08 → 30/06/12