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Abstract-Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors.

Abstract. Reaching a target object requires accurate estimation of the object spatial position and its further transformation into a suitable armmotor command. In this paper, we propose a framework that provides a robot with a capacity to represent its reachable space in an adaptive way. The location of the target is represented implicitly by both the gaze direction and the angles of arm joints. Two paired neural networks are used to compute the direct and inverse transformations between the arm position and the head position. These networks allow reaching the target either through a ballistic movement or through visually-guided actions. Thanks to the latter skill, the robot can adapt its sensorimotor transformations so as to reflect changes in its body configuration. The proposed framework was implemented on the NAO humanoid robot, and our experimental results provide evidences for its adaptative capabilities.

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