Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions.

ABSTRACT: Collaborative interactions require social robots to share the users' perspective on the interactions and adapt to the dynamics of their 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 affect-driven behaviour generation in social robots. The framework consists of (i) a hybrid neural model for evaluating facial expressions and speech of the users, forming intrinsic affective representations in the robot, (ii) an Affective Core, that employs self-organising neural models to embed behavioural traits like patience and emotional actuation that modulate the robo

SUBMITTER: Churamani N 

PROVIDER: S-EPMC8898942 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature