Abstract: The present invention creates and stores target representations in several coordinate representations based on biologically inspired models of the human vision system. By using biologically inspired target representations a computer can be programmed for robot control without using kinematics to relate a target position in camera eyes to a target position in body or head coordinates. The robot sensors and appendages are open loop controlled to focus on the target. In addition, the invention herein teaches a scenario and method to learn the mappings between coordinate representations using existing machine learning techniques such as Locally Weighted Projection Regression.

Abstract: The present disclosure describes a fused saliency map from visual and auditory saliency maps. The saliency maps are in azimuth and elevation coordinates. The auditory saliency map is based on intensity, frequency and temporal conspicuity maps. Once the auditory saliency map is determined, the map is converted into azimuth and elevation coordinates by processing selected snippets of sound from each of four microphones arranged on a robot head to detect the location of the sound source generating the saliencies.

Abstract: A method and apparatus for controlling robots based on prioritized targets extracted from fused visual and auditory saliency maps. The fused visual and auditory saliency map may extend beyond the immediate visual range of the robot yet the methods herein allow the robot to maintain an awareness of targets outside the immediate visual range. The fused saliency map may be derived in a bottom-up or top-down approach and may be feature based or object based.

Abstract: The present invention creates and stores target representations in several coordinate representations based on biologically inspired models of the human vision system. By using biologically inspired target representations a computer can be programmed for robot control without using kinematics to relate a target position in camera eyes to a target position in body or head coordinates. The robot sensors and appendages are open loop controlled to focus on the target. In addition, the invention herein teaches a scenario and method to learn the mappings between coordinate representations using existing machine learning techniques such as Locally Weighted Projection Regression.

Abstract: Described is a system for multi-mission scheduling. The system is configured to compile a list of missions, where each mission includes at least one task. Additionally, each mission has a mission value associated with it such that the mission value reflects an ordering priority of the mission. The system also compiles a list of available resources that can be utilized to complete the tasks. The resources have, varying capabilities of completing tasks. Based on the lists, the system allocates and schedules the resources to complete tasks within the missions to maximize a total mission value of completed missions. Thus, the system schedules multiple missions to maximize the value of completed missions given available resources, whereby a mission is scheduled when the totality of its tasks have been allocated a sufficient amount of resources.

Abstract: Described is a system for multi-mission scheduling. The system is configured to compile a list of missions, where each mission includes at least one task. Additionally, each mission has a mission value associated with it such that the mission value reflects an ordering priority of the mission. The system also compiles a list of available resources that can be utilized to complete the tasks. The resources have, varying capabilities of completing tasks. Based on the lists, the system allocates and schedules the resources to complete tasks within the missions to maximize a total mission value of completed missions. Thus, the system schedules multiple missions to maximize the value of completed missions given available resources, whereby a mission is scheduled when the totality of its tasks have been allocated a sufficient amount of resources.