Enhancing team performance with transfer-learning during real-world human-robot collaboration

TL;DR

This study demonstrates that transfer learning in deep reinforcement learning agents significantly improves real-world human-robot collaboration efficiency and perceived fluency, reducing training time and enhancing team performance.

Contribution

The paper introduces the integration of transfer learning with deep reinforcement learning for socially aware robots in real-world collaboration tasks.

Findings

Transfer learning halved training time for new participants.

Transfer learning improved subjective team performance and fluency.

Objective and subjective performance metrics often did not correlate.

Abstract

Socially aware robots should be able, among others, to support fluent human-robot collaboration in tasks that require interdependent actions in order to be solved. Towards enhancing mutual performance, collaborative robots should be equipped with adaptation and learning capabilities. However, co-learning can be a time consuming procedure. For this reason, transferring knowledge from an expert could potentially boost the overall team performance. In the present study, transfer learning was integrated in a deep Reinforcement Learning (dRL) agent. In a real-time and real-world set-up, two groups of participants had to collaborate with a cobot under two different conditions of dRL agents; one that was transferring knowledge and one that did not. A probabilistic policy reuse method was used for the transfer learning (TL). The results showed that there was a significant difference between the performance of the two groups; TL halved the time needed for the training of new participants to the task. Moreover, TL also affected the subjective performance of the teams and enhanced the perceived fluency. Finally, in many cases the objective performance metrics did not correlate with the subjective ones providing interesting insights about the design of transparent and explainable cobot behaviour.