Bootstrap Learning and Visual Processing Management on Mobile Robots

Abstract

A central goal of robotics and AI is to enable a team of robots to operate autonomously in the real world and collaborate with humans over an extended period of time. Though developments in sensor technology have resulted in the deployment of robots in specific applications the ability to accurately sense and interact with the environment is still missing. Key challenges to the widespread deployment of robots include the ability to learn models of environmental features based on sensory inputs, bootstrap off of the learned models to detect and adapt to environmental changes, and autonomously tailor the sensory processing to the task at hand. This paper summarizes a comprehensive effort towards such bootstrap learning, adaptation, and processing management using visual input. We describe probabilistic algorithms that enable a mobile robot to autonomously plan its actions to learn models of color distributions and illuminations. The learned models are used to detect and adapt to illumination changes. Furthermore, we describe a probabilistic sequential decision-making approach that autonomously tailors the visual processing to the task at hand. All algorithms are fully implemented and tested on robot platforms in dynamic environments.