Human-Robot Mutual Learning through Affective-Linguistic Interaction and Differential Outcomes Training [Pre-Print]

TL;DR

This study explores how affective-linguistic communication combined with differential outcomes training enhances mutual learning between humans and robots, showing significant improvements in learning accuracy and speed, with implications for therapeutic and educational robot applications.

Contribution

It demonstrates that differential outcomes training and exploration-exploitation policies significantly improve human-robot mutual learning, a novel approach in affective-linguistic interaction research.

Findings

Mutual learning is significantly better with Differential Outcomes Training (DOT).

Robots using exploration-exploitation policies learn faster and more accurately.

Implications for socially assistive robots in therapy and education.

Abstract

Owing to the recent success of Large Language Models, Modern A.I has been much focused on linguistic interactions with humans but less focused on non-linguistic forms of communication between man and machine. In the present paper, we test how affective-linguistic communication, in combination with differential outcomes training, affects mutual learning in a human-robot context. Taking inspiration from child-caregiver dynamics, our human-robot interaction setup consists of a (simulated) robot attempting to learn how best to communicate internal, homeostatically-controlled needs; while a human "caregiver" attempts to learn the correct object to satisfy the robot's present communicated need. We studied the effects of i) human training type, and ii) robot reinforcement learning type, to assess mutual learning terminal accuracy and rate of learning (as measured by the average reward achieved by the robot). Our results find mutual learning between a human and a robot is significantly improved with Differential Outcomes Training (DOT) compared to Non-DOT (control) conditions. We find further improvements when the robot uses an exploration-exploitation policy selection, compared to purely exploitation policy selection. These findings have implications for utilizing socially assistive robots (SAR) in therapeutic contexts, e.g. for cognitive interventions, and educational applications.