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)

Abstract

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

Keywords

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

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