RI: Small: To Ask or Not to Ask - A Foundation for the Optimization of Human-Robot Networks

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A team of unmanned vehicles can have tremendous impact in areas such as disaster relief, emergency response, and national security. Human operators are an integral part of these missions. Given the complexity of the tasks, harshness of the operation environments, and resource limitations, a fundamental understanding of how humans and robots can best interact is crucial to a successful operation. This proposal introduces a new multidisciplinary approach for efficient human-robot collaboration on tasks that require a visual search in the presence of uncertainty. The proposed methodology for characterizing human visual performance also contributes to the field of human perception and cognitive psychology. This proposal also has a significant educational component targeting under-represented students of the central California area and UC Santa Barbara.A new methodology is proposed for predicting whether people can make a correct visual decision for a given sensory input. The approach is based on training convolutional neural networks using extensive input from Amazon Mechanical Turk workers. This foundational understanding of human visual performance will have significant implication for robotic field decision making. A new set of mathematical tools will be developed for robotic field operation, given a proper understanding of human visual capabilities. This enables a robot to properly decide when to seek human help, when to rely on itself, and when to sense more. Finally, the cost of communication is characterized, due to the space-varying nature of link quality, which can significantly affect seeking human help. The proposed theories and design paradigms will be validated using a robotic testbed.