A Socially Adaptable Framework for Human-Robot Interaction.
A Socially Adaptable Framework for Human-Robot Interaction.
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DOI：
10.3389/frobt.2020.00121
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发表时间：
2020
影响因子：
3.4
通讯作者：
Sciutti A
中科院分区：
文献类型：
--
作者：
Tanevska A;Rea F;Sandini G;Cañamero L;Sciutti A
在我们的日常生活中，我们经常与同伴进行复杂的，个性化的和适应性的互动。为了重现同样丰富的、类似人类的互动，社交机器人应该意识到我们的需求和情感状态，并不断调整自己的行为。我们提出的解决方案是让机器人学习如何选择行为，以最大限度地提高其同伴的互动愉快度。为了使机器人自主决策，这个过程可以由内部激励系统来指导。我们希望研究这种自适应机器人框架将如何为不同的用户提供功能和个性化。我们还希望探索适应性和个性化是否会为人机交互（HRI）带来任何额外的丰富性，或者它是否会带来不被机器人的人类同伴接受的不确定性和不可预测性。为此，我们为人形机器人iCub设计了一个社会适应框架。因此，机器人感知并重新使用来自人的情感和交互信号作为基于内部社会动机的适应的输入。我们努力调查的价值，在我们的框架中所产生的适应HRI的上下文中。特别是，我们比较了用户将如何体验与自适应与非自适应社交机器人的互动。为了解决这些问题，我们提出了一个比较互动的研究与iCub，用户作为机器人的看护人，和iCub的社会适应是由内部舒适度的变化，iCub从其看护人收到的刺激。我们调查和比较iCub的内部动态将如何被人们所感知，无论是在一个条件下，当iCub不个性化其行为的人，而在一个条件下，它是自适应的。最后，我们建立了一个自适应框架可能带来的潜在好处的上下文中的重复与人形机器人的互动。
In our everyday lives we regularly engage in complex, personalized, and adaptive interactions with our peers. To recreate the same kind of rich, human-like interactions, a social robot should be aware of our needs and affective states and continuously adapt its behavior to them. Our proposed solution is to have the robot learn how to select the behaviors that would maximize the pleasantness of the interaction for its peers. To make the robot autonomous in its decision making, this process could be guided by an internal motivation system. We wish to investigate how an adaptive robotic framework of this kind would function and personalize to different users. We also wish to explore whether the adaptability and personalization would bring any additional richness to the human-robot interaction (HRI), or whether it would instead bring uncertainty and unpredictability that would not be accepted by the robot's human peers. To this end, we designed a socially adaptive framework for the humanoid robot iCub. As a result, the robot perceives and reuses the affective and interactive signals from the person as input for the adaptation based on internal social motivation. We strive to investigate the value of the generated adaptation in our framework in the context of HRI. In particular, we compare how users will experience interaction with an adaptive versus a non-adaptive social robot. To address these questions, we propose a comparative interaction study with iCub whereby users act as the robot's caretaker, and iCub's social adaptation is guided by an internal comfort level that varies with the stimuli that iCub receives from its caretaker. We investigate and compare how iCub's internal dynamics would be perceived by people, both in a condition when iCub does not personalize its behavior to the person, and in a condition where it is instead adaptive. Finally, we establish the potential benefits that an adaptive framework could bring to the context of repeated interactions with a humanoid robot.