Hand-eye coordination is an important motor skill acquired in infancy which precedes pointing behavior. Pointing facilitates social interactions by directing attention of engaged participants. It is thus essential for the natural flow of human-robot interaction. Here, we attempt to explain how pointing emerges from sensorimotor learning of hand-eye coordination in a humanoid robot. During a body babbling phase with a random walk strategy, a robot learned mappings of joints for different arm postures. Arm joint configurations were used to train biologically inspired models consisting of SOMs. We show that such a model implemented on a robotic platform accounts for pointing behavior while humans present objects out of reach of the robot's hand.
In REF , they use 2D SOMs in order to realize the learning of hand-eye coordination.
Learning hand-eye coordination for a humanoid robot using SOMs
HRI '14
Computer Science