DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This work addresses the problem of enabling robots to autonomously interact and learn in dynamic environments, which is incremental as it builds on existing theories and algorithms.
The paper introduces a cognitive architecture for humanoid robots that enables proactive exploration and manipulation, integrating perception, motor learning, planning, and memory to solve symbol grounding and acquire language capabilities. It was validated in real-time human-robot interaction experiments with the iCub robot, showing applicability with naive users.
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.