Using learning to control artificial avatars in human motor coordination tasks
This work addresses the challenge of designing human-like cyber-agents for cooperative interaction in healthcare, particularly for motor rehabilitation.
The paper proposes a method combining Markov Chains and reinforcement learning to create artificial avatars that mimic human motor coordination, aiming to improve rehabilitation strategies for patients with motor disorders.
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.