Affordance-based altruistic robotic architecture for human–robot collaboration

Abstract

This article proposes a computational model for altruistic behavior, shows its implementation on a physical robot, and presents the results of human–robot interaction experiments conducted with the implemented system. Inspired from the sensorimotor mechanisms of the primate brain, object affordances are utilized for both intention estimation and action execution, in particular, to generate altruistic behavior. At the core of the model is the notion that sensorimotor systems developed for movement generation can be used to process the visual stimuli generated by actions of the others, infer the goals behind, and take the necessary actions to help achieving these goals, potentially leading to the emergence of altruistic behavior. Therefore, we argue that altruistic behavior is not necessarily a consequence of deliberate cognitive processing but may emerge through basic sensorimotor processes such as error minimization, that is, minimizing the difference between the observed and expected outcomes. In the model, affordances also play a key role by constraining the possible set of actions that an observed actor might be engaged in, enabling a fast and accurate intention inference. The model components are implemented on an upper-body humanoid robot. A set of experiments are conducted validating the workings of the components of the model, such as affordance extraction and task execution. Significantly, to assess how human partners interact with our altruistic model deployed robot, extensive experiments with naïve subjects are conducted. Our results indicate that the proposed computational model can explain emergent altruistic behavior in reference to its biological counterpart and moreover engage human partners to exploit this behavior when implemented on an anthropomorphic robot.