Trust in robot-robot scaffolding

Murat Kirtay, Verena V. Hafner, Minoru Asada, Erhan Oztop

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1841-1852
Number of pages12
JournalIEEE Transactions on Cognitive and Developmental Systems
Issue number4
Publication statusPublished - 10 Jan 2023


  • Cognitive load
  • decision making
  • robot trust
  • scaffolding
  • visual recalling


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