DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self (1706.03661v2)
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.
- Clément Moulin-Frier (35 papers)
- Tobias Fischer (125 papers)
- Maxime Petit (5 papers)
- Grégoire Pointeau (1 paper)
- Jordi-Ysard Puigbo (4 papers)
- Ugo Pattacini (11 papers)
- Sock Ching Low (1 paper)
- Daniel Camilleri (1 paper)
- Phuong Nguyen (27 papers)
- Matej Hoffmann (49 papers)
- Hyung Jin Chang (47 papers)
- Martina Zambelli (8 papers)
- Anne-Laure Mealier (1 paper)
- Andreas Damianou (28 papers)
- Giorgio Metta (20 papers)
- Tony J. Prescott (4 papers)
- Yiannis Demiris (26 papers)
- Peter Ford Dominey (8 papers)
- Paul F. M. J. Verschure (8 papers)