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Designing artificial cyber-agents able to interact with human safely, smartlyand in a natural way is a current open problem in control. Solving such anissue will allow the design of cyber-agents capable of co-operativelyinteracting with people in order to fulfil common joint tasks in a multitude ofdifferent applications. This is particularly relevant in the context ofhealthcare applications. Indeed, the use has been proposed of artificial agentsinteracting and coordinating their movements with those of a patient sufferingfrom social or motor disorders. Specifically, it has been shown that anartificial agent exhibiting certain kinematic properties could provideinnovative and efficient rehabilitation strategies for these patients.Moreover, it has also been shown that the level of motor coordination isenhanced if these kinematic properties are similar to those of the individualit is interacting with. In this paper we discuss, first, a new method based onMarkov Chains to confer "human motor characteristics" on a virtual agent, so asthat it can coordinate its motion with that of a target individual whileexhibiting specific kinematic properties. Then, we embed such synthetic modelin a control architecture based on reinforcement learning to synthesize acyber-agent able to mimic the behaviour of a specific human performing a jointmotor task with one or more individuals.

Using learning to control artificial avatars in human motor coordination tasks