Automatic Gaze Control of Humanoid Head

This paper addresses the problem of gaze control for a humanoid robot head. We propose a control framework composed of two components: 1. an adaptive visual tracker that is capable of tracking an object with unknown trajectory and 2. an optimised visual control framework capable of controlling the overall head joint motions in order to maintain the tracked object at image centre. The advantage of such a framework is that it doesn’t require any prior knowledge of the object trajectory and can optimally exhibit the required joint motions i.e. the maximum motion of neck yaw and eye tilt/pan when gazing in a horizontal plane. An adaptive gain has been used with the controller in order to provide dynamic convergence of the task space error when gazing at an object with unpredicted trajectories. The proposed framework has been validated in real-time using our bi-manual robotic platform Boris and the obtained results demonstrate the efficiency of our approach.