Inferring Operator Emotions from a Motion-Controlled Robotic Arm

TL;DR

This paper presents a machine learning approach to infer a remote robot operator's emotional state from the robot's movements, achieving over 83% accuracy without requiring additional sensors or user effort.

Contribution

It introduces a novel method for emotion recognition in remote robot control using only the robot's functional hand movements, bypassing traditional vital sign or body language measures.

Findings

Achieved 83.3% accuracy in emotion recognition

Demonstrated feasibility of using robot movements for affective state inference

Discussed implications for remote robot operation and affective robotics

Abstract

A remote robot operator's affective state can significantly impact the resulting robot's motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator's affective states in remote robot control scenarios is, however, underexplored. Current emotion recognition methods rely on reading the user's vital signs or body language, but the devices and user participation these measures require would add limitations to remote robot control. We demonstrate that the functional movements of a remote-controlled robotic avatar, which was not designed for emotional expression, can be used to infer the emotional state of the human operator via a machine-learning system. Specifically, our system achieved 83.3$\%$ accuracy in recognizing the user's emotional state expressed by robot movements, as a result of their hand motions. We discuss the implications of this system on prominent current and future remote robot operation and affective robotic contexts.