A Novel Mirror Neuron Inspired Decision-Making Architecture for Human–Robot Interaction

Abstract

Inspired by the role of mirror neurons and the importance of predictions in joint action, a novel decision-making structure is proposed, designed and tested for both individual and dyadic action. The structure comprises models representing individual decision policies, policy integration layer(s), and a negotiation layer. The latter is introduced to prevent and resolve conflicts among individuals through internal simulation rather than via explicit agent-agent communication. As the main modelling tool, Dynamic Neural Fields (DNFs) were chosen. Data was captured from human-human experiments with a decision-making task performed by either one or two participants. The task involves choosing and picking blocks one by one from seven wooden blocks to create an alpha/numeric character on a 7-segment. The task is designed to be as generic as possible. Recorded hand and blocks movements were used for developing DNF-based models by optimising parameters using a genetic algorithm. Results show that decision policies can be modelled and integrated with acceptable accuracy for individual performances. In the dyadic experiment, using only individual models without the negotiation layer, the model failed to resolve conflicts. However, with the implementation of a negotiation layer, this problem could be overcome. The proposed decision-making structure based on DNFs is developed and tested for a simple pick-and-place task. However, the main primitive underlying action of this task, pick-and-place, is indeed part of many more complex tasks people perform in their day-to-day life. Paired with the possibility to gradually evolve the architecture by adding new policies on demand, the architecture provides a general framework for modelling decision-making in joint action tasks.