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dc.contributor.advisorRamamoorthy, Subramanian
dc.contributor.advisorArvind, D K
dc.contributor.authorValtazanos, Aris
dc.date.accessioned2013-11-08T15:47:19Z
dc.date.available2013-11-08T15:47:19Z
dc.date.issued2013-11-28
dc.identifier.urihttp://hdl.handle.net/1842/8091
dc.description.abstractRecent developments in robot technology have contributed to the advancement of autonomous behaviours in human-robot systems; for example, in following instructions received from an interacting human partner. Nevertheless, increasingly many systems are moving towards more seamless forms of interaction, where factors such as implicit trust and persuasion between humans and robots are brought to the fore. In this context, the problem of attaining, through suitable computational models and algorithms, more complex strategic behaviours that can influence human decisions and actions during an interaction, remains largely open. To address this issue, this thesis introduces the problem of decision shaping in strategic interactions between humans and robots, where a robot seeks to lead, without however forcing, an interacting human partner to a particular state. Our approach to this problem is based on a combination of statistical modeling and synthesis of demonstrated behaviours, which enables robots to efficiently adapt to novel interacting agents. We primarily focus on interactions between autonomous and teleoperated (i.e. human-controlled) NAO humanoid robots, using the adversarial soccer penalty shooting game as an illustrative example. We begin by describing the various challenges that a robot operating in such complex interactive environments is likely to face. Then, we introduce a procedure through which composable strategy templates can be learned from provided human demonstrations of interactive behaviours. We subsequently present our primary contribution to the shaping problem, a Bayesian learning framework that empirically models and predicts the responses of an interacting agent, and computes action strategies that are likely to influence that agent towards a desired goal. We then address the related issue of factors affecting human decisions in these interactive strategic environments, such as the availability of perceptual information for the human operator. Finally, we describe an information processing algorithm, based on the Orient motion capture platform, which serves to facilitate direct (as opposed to teleoperation-mediated) strategic interactions between humans and robots. Our experiments introduce and evaluate a wide range of novel autonomous behaviours, where robots are shown to (learn to) influence a variety of interacting agents, ranging from other simple autonomous agents, to robots controlled by experienced human subjects. These results demonstrate the benefits of strategic reasoning in human-robot interaction, and constitute an important step towards realistic, practical applications, where robots are expected to be not just passive agents, but active, influencing participants.en_US
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en_US
dc.language.isoenen_US
dc.publisherThe University of Edinburghen_US
dc.relation.hasversionA. Valtazanos, D.K. Arvind, and S. Ramamoorthy. Using wearable inertial sensors for posture and position tracking in unconstrained environments through learned translation manifolds. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2013.en_US
dc.relation.hasversionA. Valtazanos and S. Ramamoorthy. Bayesian interaction shaping: learning to influence strategic interactions in mixed robotic domains. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013.en_US
dc.relation.hasversionA. Valtazanos and S. Ramamoorthy, Evaluating the effects of limited perception on interactive decisions in mixed robotic domains, ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2013.en_US
dc.relation.hasversionA. Valtazanos, D.K. Arvind, and S. Ramamoorthy. Latent space segmentation for mobile gait analysis. ACM Transactions on Embedded Computing Systems 12(4), 2013.en_US
dc.relation.hasversionA. Valtazanos and S. Ramamoorthy. Intent inference and strategic escape in multi-robot games with physical limitations and uncertainty. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011.en_US
dc.relation.hasversionA. Valtazanos and S. Ramamoorthy. Online motion planning for multi-robot interaction using composable reachable sets. RoboCup International Symposium – Springer Verlag Lecture Notes in Artificial Intelligence, 2011.en_US
dc.relation.hasversionA. Valtazanos and S. Ramamoorthy. NaOISIS: A 3-D behavioural simulator for the NAO humanoid robot. RoboCup International Symposium – Springer Verlag Lecture Notes in Artificial Intelligence, 2011.en_US
dc.relation.hasversionA. Valtazanos, D.K. Arvind, and S. Ramamoorthy. Comparative study of segmentation of periodic motion data for mobile gait analysis. ACM International Conference on Wireless Health, 2010.en_US
dc.subjectroboticsen_US
dc.subjectmachine learningen_US
dc.subjecthuman-robot interactionen_US
dc.subjectwireless sensor networksen_US
dc.titleDecision shaping and strategy learning in multi-robot interactionsen_US
dc.typeThesis or Dissertationen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US


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