Autonomous view selection and gaze stabilization for humanoid robots

To increase the autonomy of humanoid robots, the visual perception must support the efficient collection and interpretation of visual scene cues by providing task-dependent information. Active vision systems allow to extend the observable workspace by employing active gaze control, i.e. by shifting the gaze to relevant areas in the scene. When moving the eyes, stabilization of the camera images is crucial for successful task execution. In this paper, we present an active vision system for task-oriented selection of view directions and gaze stabilization to enable a humanoid robot to robustly perform vision-based tasks. We investigate the interaction between a gaze stabilization controller and view planning to select the next best view direction based on saliency maps which encode task-relevant information. We demonstrate the performance of the systems in a real world scenario, in which a humanoid robot is performing vision-based grasping while moving, a task that would not be possible without the combination of view selection and gaze stabilization