  Designing artificial cyber-agents able to interact with human 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 human performing a joint
motor task with one or more individuals.
