Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702209
Title: Communication, learning, and touch
Author: McGovern, Patrick
ISNI:       0000 0004 6056 8255
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2016
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Abstract:
This thesis is concerned with the challenge of creating a robot which is capable of natural, verbal communication with humans. More specifically, it considers the task of categorising objects according to sensory stimuli. We focus on tactile texture perception, a sensory feature which has received relatively little attention in artificial intelligence, in comparison with vision and audition. Through a multidisciplinary approach involving sensory feature extraction, computer simulations, and psychophysical experiments, we compare texture perception and categorisation between human and robot, and consider the problem of enabling communication between them. We begin by presenting TacTip, an artificial fingertip sensor which we apply to the task of texture recognition. We describe a feature extraction process used to specify a textural feature space for the sensor, which is then used for texture recognition and categorisation. Next, we present a framework for robotic communication and learning. This framework consists of two main parts, the first of which is the representational model used by the robot to categorise perceived stimuli. We present a model based on random set theory and prototype theory, and compare this with a similar model based on Bayesian statistics. The second part of the framework is the context in which the robots communicate and learn. In our case this consists of a multi-agent simulation in which robots communicate with each other through pairwise interactions called language games, and thereby develop a shared set of categories. Finally, we consider how our robot might communicate with and learn from humans. We describe two psychophysical experiments, the first of which studies how humans naturally classify textures, the second investigating whether humans can learn specific categorisations presented to them. Each experiment can be interpreted as one part of a language game interaction between human and robot. We discuss our results in the context of human-robot communication.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.702209  DOI: Not available
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