DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

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 human and 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.

I. Introduction

The so-called symbol grounding problem (SGP) [1]–[3] refers to the way in which a cognitive agent forms an internal and unified representation of an external word referent from the continuous flow of low-level sensorimotor data generated by its interaction with the environment. In this paper, we focus on solving the SGP in the context of human-robot interaction (HRI), where a humanoid iCub robot [4] acquires and expresses knowledge about the world by interacting with a human partner. Solving the SGP is of particular relevance in HRI, where a repertoire of shared symbolic units forms the basis of an efficient linguistic communication channel between the robot and the human.