Developing grounded representations for robots through the principles of sensorimotor coordination
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2014
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Resumo |
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. |
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application/pdf |
Identificador | |
Publicador |
Queensland University of Technology |
Relação |
http://eprints.qut.edu.au/71763/1/Arren_Glover_Thesis.pdf Glover, Arren John (2014) Developing grounded representations for robots through the principles of sensorimotor coordination. PhD thesis, Queensland University of Technology. |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #Robotics #Affordance #Visual Object Recognition #Symbol Grounding #Markov Decision Process |
Tipo |
Thesis |