Trust in robot-robot scaffolding
Trust in robot-robot scaffolding
复制标题
对机器人脚手架的信任
DOI：
10.1109/tcds.2023.3235974
复制
发表时间：
2023
影响因子：
5
通讯作者：
Oztop Erhan
中科院分区：
文献类型：
--
作者：
Kirtay Murat;Hafner Verena V.;Asada Minoru;Oztop Erhan
机器人对人类和其他代理人的信任研究并没有得到广泛的探讨，尽管它对不久的将来的人类-机器人共生社会的重要性。在这里，我们建议机器人应该信任那些倾向于减少计算负荷的伙伴，这类似于人类的认知负荷。我们通过一个交互式的视觉回忆任务来测试这个想法。在第一组实验中，机器人可以从具有不同指导策略的在线教师那里获得帮助，以根据实验期间经历的计算负载来决定应该信任哪一个。第二组实验涉及机器人-机器人交互。类似于机器人在线教师的案例，Pepper机器人被要求为能力较弱的“婴儿”机器人（Nao）进行学习，无论是否配备心理理论和任务经验记忆的认知能力，以评估这些认知能力对脚手架性能的贡献。总体而言，结果表明，机器人信任的基础上计算/认知负荷的顺序决策框架内，导致有效的合作伙伴选择和机器人-机器人脚手架。因此，使用由机器人的认知处理引起的计算负荷可以用作用于评估交互伙伴的可信度的内部信号。
The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human–robot symbiotic societies. Here, we propose that robots should trust partners that tend to reduce their computational load, which is analogous to human cognitive load. We test this idea by adopting an interactive visual recalling task. In the first set of experiments, the robot can get help from online instructors with different guiding strategies to decide which one it should trust based on the computational load it experiences during the experiments. The second set of experiments involves robot–robot interactions. Akin to the robot–online instructor case, the Pepper robot is asked to scaffold the learning of a less capable “infant” robot (Nao) with or without being equipped with the cognitive abilities of theory of mind and task experience memory to assess the contribution of these cognitive abilities to scaffolding performance. Overall, the results show that robot trust based on computational/cognitive load within a sequential decision-making framework leads to effective partner selection and robot–robot scaffolding. Thus, using the computational load incurred by the cognitive processing of a robot may serve as an internal signal for assessing the trustworthiness of interaction partners.