Trust in Robot–Robot Scaffolding

Abstract

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 decisionmaking 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.