Evaluating a Decision-Making Architecture in Human-Robot Collaboration Experiments

A novel decision-making architecture inspired by the role of mirror neurons is evaluated in two user studies. First, the role and efficacy of the negotiation layer of the architecture is assessed. Then, in a “Wizard of Oz” experiment, the performance of the complete architecture is compared with the one of a human decision-maker. The same task of using wooden blocks to create characters in a kind of mechanical model of a 7-segment display is used in both experiments (task details in Sect. 2.4). The same task is used to capture data and train policy modules, an integration module, and a negotiation layer to be combined into decision-making models of involved agents, which build upon our previous work. The evaluation results show a significant improvement in terms of the chosen objective and subjective measures when the robot uses the complete architecture with the negotiation layer. No significant difference was found for any of the measures when comparing the human decision-maker and the complete model. Although the robot with a human decision-maker scored descriptively slightly better in all measures, a further Bayesian comparison of the data suggests a high probability of similarity between the model and the human decision-maker. This was further illustrated by a qualitative analysis of the post-experiment interview questions; in answering the third question, where 17 participants identified that the robot using the complete model was like working with a human, and an equal number opted for identifying the robot controlled by a human decision-maker. In addition, answering the first question, 6 participants found no difference between the robot being controlled by a human decision-maker and being controlled using the complete model.