Unveiling the Learning Curve: Enhancing Transparency in Robot’s Learning with Inner Speech and Emotions

Abstract

The lack of transparency in robotic learning processes poses a significant challenge to effective human-robot collaboration. This is particularly relevant in non-industrial settings because it prevents humans from adequately comprehending a robot’s intentions, progress, and decision-making rationale, which is essential for seamless interaction. To address this issue, this work presents a study where users observe a robot endowed with three distinct emotional/behavioural mechanisms for conveying transparent information about its learning process. The proposed mechanisms use inner speech, emotions, and a combination of the two communication styles (hybrid). To assess and evaluate the transparency of these behavioural models, a between-subject study was conducted with 108 participants. Results indicate that the people’s perception of the robot’s warmth dimension increased when it utilized a hybrid model to explain its learning state. Additionally, increased transparency was observed when the robot used inner speech during the learning process.