论文标题
在现实世界中的人类机器人合作期间通过转会学习提高团队绩效
Enhancing team performance with transfer-learning during real-world human-robot collaboration
论文作者
论文摘要
具有社会意识的机器人应该能够在需要相互依存的行动才能解决的任务中支持流利的人类机器人协作。为了提高相互绩效，应配备适应性和学习能力的协作机器人。但是，共学习可能是一个耗时的程序。因此，从专家转移知识可能会提高整个团队的绩效。在本研究中，转移学习被整合到深入的增强学习（DRL）代理中。在实时和现实世界中，两组参与者不得不在DRL代理的两个不同条件下与Cobot合作。一种传递知识的人，一个没有。转移学习（TL）使用了一种概率策略重用方法。结果表明，两组的表现之间存在显着差异。 TL将培训新参与者完成任务所需的时间减半。此外，TL还影响了团队的主观表现，并增强了感知的流利度。最后，在许多情况下，客观绩效指标与主观绩效指标无关，而主观的指标提供了有关透明和可解释的配件行为设计的有趣见解。
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.
