DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self

Clement Moulin-frier, Tobias Fischer, Maxime Petit, Gregoire Pointeau, Jordi-ysard Puigbo, Ugo Pattacini, Sock Ching Low, Daniel Camilleri, Phuong Nguyen, Matej Hoffmann, Hyung Jin Chang, Martina Zambelli, Anne-laure Mealier, Andreas Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.
Original languageEnglish
Number of pages18
JournalIEEE Transactions on Cognitive and Developmental Systems
Early online date18 Sept 2017
Publication statusE-pub ahead of print - 18 Sept 2017


  • Cognitive Robotics
  • Distributed Adaptive Control
  • Human-Robot Interaction
  • Symbol grounding
  • Autobiographical memory


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