Learning to predict the behaviour of deformable objects through and for robotic interaction

Arriola Rios, Veronica Esther (2013). Learning to predict the behaviour of deformable objects through and for robotic interaction. University of Birmingham. Ph.D.

PDF - Accepted Version

Download (5MB)


Every day environments contain a great variety of deformable objects and it is not possible to program a robot in advance to know about their characteristic behaviours. For this reason, robots have been highly successful in manoeuvring deformable objects mainly in the industrial sector, where the types of interactions are predictable and highly restricted, but research in everyday environments remains largely unexplored. The contributions of this thesis are: i) the application of an elastic/plastic mass-spring method to model and predict the behaviour of deformable objects manipulated by a robot; ii) the automatic calibration of the parameters of the model, using images of real objects as ground truth; iii) the use of piece-wise regression curves to predict the reaction forces, and iv) the use of the output of this force prediction model as input for the mass-spring model which in turn predicts object deformations; v) the use of the obtained models to solve a material classification problem, where the robot must recognise a material based on interaction with it.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
Sloman, Aaronaxs@cs.bham.ac.ukUNSPECIFIED
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Computer Science
Funders: None/not applicable
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
URI: http://etheses.bham.ac.uk/id/eprint/4636


Request a Correction Request a Correction
View Item View Item


Downloads per month over past year