Developing grounded representations for robots through the principles of sensorimotor coordination

Glover, Arren John (2014) Developing grounded representations for robots through the principles of sensorimotor coordination. PhD thesis, Queensland University of Technology.

Abstract

Robots currently recognise and use objects through algorithms that are hand-coded or specifically trained. Such robots can operate in known, structured environments but cannot learn to recognise or use novel objects as they appear. This thesis demonstrates that a robot can develop meaningful object representations by learning the fundamental relationship between action and change in sensory state; the robot learns sensorimotor coordination. Methods based on Markov Decision Processes are experimentally validated on a mobile robot capable of gripping objects, and it is found that object recognition and manipulation can be learnt as an emergent property of sensorimotor coordination.

Impact and interest:

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132 since deposited on 02 Jun 2014
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ID Code: 71763
Item Type: QUT Thesis (PhD)
Supervisor: Wyeth, Gordon & Corke, Peter
Keywords: Robotics, Affordance, Visual Object Recognition, Symbol Grounding, Markov Decision Process
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 02 Jun 2014 02:23
Last Modified: 09 Sep 2015 05:42

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