Augmenting the Reachable Space in the NAO Humanoid Robot
Marco Antonelli, Beata J. Grzyb, Vicente Ubet Castelló, Angel Pasqual del Pobil · 2012
Reaching for a target requires estimating the spatial po-sition of the target and to convert such a position in a suitable arm-motor command. In the proposed frame-work, the location of the target is represented implic-itly by the gaze direction of the robot and by the dis-tance of the target. The NAO robot is provided with two cameras, one to look ahead and one to look down, which constitute two independent head-centered coor-dinate systems. These head-centered frames of refer-ence are converted into reaching commands by two neu-ral networks. The weights of networks are learned by moving the arm while gazing the hand, using an on-line learning algorithm that maintains the covariance ma-trix of weights. This work adapts a previously proposed model that worked on a full humanoid robot torso, to work with the NAO and is a step toward a more generic framework for the implicit representation of the periper-sonal space in humanoid robots.