Affect-Driven Modelling of Robot Personality for Collaborative Human-Robot Interactions
This work addresses the need for more dynamic and personality-based robot behavior in human-robot interactions, though it is incremental as it builds on existing affective modeling approaches.
The paper tackles the problem of generating adaptive affective behavior in social robots for collaborative interactions by proposing a personality-driven framework, resulting in a user study where participants perceived different robot personalities influencing negotiation strategies in the Ultimatum Game.
Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between observed human expressions and static robot actions. In this paper, we propose a novel framework for personality-driven behaviour generation in social robots. The framework consists of (i) a hybrid neural model for evaluating facial expressions and speech, forming intrinsic affective representations in the robot, (ii) an Affective Core, that employs self-organising neural models to embed robot personality traits like patience and emotional actuation, and (iii) a Reinforcement Learning model that uses the robot's affective appraisal to learn interaction behaviour. For evaluation, we conduct a user study (n = 31) where the NICO robot acts as a proposer in the Ultimatum Game. The effect of robot personality on its negotiation strategy is witnessed by participants, who rank a patient robot with high emotional actuation higher on persistence, while an inert and impatient robot higher on its generosity and altruistic behaviour.