User-aware collaborative learning in human-robot interactions

Our work investigates how social robots can efficiently collaborate with human users in a user-aware manner, minimising the generated frustration in human colleagues, thus enhancing their experience. As part of this, we develop a user-aware framework for human-robot collaborative learning. We model users’ frustration during human-robot interactions based on recent interactions inspired by Psychological principles and develop different frustration-aware interactive preference learning and decision-making models using multi-armed bandit and knapsack methods. Evaluating our approach, 1) we conducted simulated experiments on realistic human-behaviour datasets and 2) a user-study in which participants worked with a TIAGo Steel humanoid robot on a collaboration task using frustration- aware and non frustration-aware (Upper Confidence Bounds and Instruction-based) models. We demonstrate that when collaborating with the frustration-aware robot, users completed the collaboration task 9.04% faster and using 20.54% less number of verbal interactions, with user questionnaire responses reporting less frustration experienced compared to the baseline approaches. Additionally, we create a multimodal dataset containing over 6 hours of human-robot interactions displaying various explicit and implicit user responses.