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

Research output: Contribution to journalArticle


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

Colleges, School and Institutes


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 Sep 2017
Publication statusE-pub ahead of print - 18 Sep 2017


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