A MultiModal Social Robot Toward Personalized Emotion Interaction

TL;DR

This paper presents a multimodal social robot framework using reinforcement learning to personalize emotional interactions, aiming to improve naturalness and engagement in human-robot interactions.

Contribution

It introduces a novel multimodal HRI framework that leverages reinforcement learning to adapt robot behaviors based on human emotional cues.

Findings

Enhanced engagement through personalized emotional responses

Effective use of multimodal data for emotion recognition

Improved interaction quality in social scenarios

Abstract

Human emotions are expressed through multiple modalities, including verbal and non-verbal information. Moreover, the affective states of human users can be the indicator for the level of engagement and successful interaction, suitable for the robot to use as a rewarding factor to optimize robotic behaviors through interaction. This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy and personalize emotional interaction for a human user. The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework.