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• W2896300364 abstract "Designing artificial cyber-agents able to interact with safely, smartly and in a natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of co-operatively interacting with people in order to fulfil common joint tasks in a multitude of different applications. This is particularly relevant in the context of healthcare applications. Indeed, the use has been proposed of artificial agents interacting and coordinating their movements with those of a patient suffering from social or motor disorders. Specifically, it has been shown that an artificial agent exhibiting certain kinematic properties could provide innovative and efficient rehabilitation strategies for these patients. Moreover, it has also been shown that the level of motor coordination is enhanced if these kinematic properties are similar to those of the individual it is interacting with. In this paper we discuss, first, a new method based on Markov Chains to confer human motor characteristics on a virtual agent, so as that it can coordinate its motion with that of a target individual while exhibiting specific kinematic properties. Then, we embed such synthetic model in a control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behaviour of a specific performing a joint motor task with one or more individuals." @default.
• W2896300364 created "2018-10-26" @default.
• W2896300364 creator A5008937640 @default.
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• W2896300364 date "2018-10-09" @default.
• W2896300364 modified "2023-09-27" @default.
• W2896300364 title "Using learning to control artificial avatars in human motor coordination tasks" @default.
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• W2896300364 hasPublicationYear "2018" @default.
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