RI-Small: Statistical Relational Models for Semantic Robot Mapping

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How can we build robots that are able to distinguish and handle the many objects located in our everyday environments? And how can we endow these robots with the ability to reason about spatial concepts such as rooms, hallways, streets, and intersections? Even though the robotics community has made tremendous progress in the development of efficient techniques for representing and dealing with noisy sensor information, current techniques do not have the expressive power to address these questions. In this project, we will develop statistical relational machine learning techniques that are able to extract high-level concepts from robotic sensor data. By transferring knowledge learned in other environments, our techniques will enable robots to recognize objects and places in previously unseen environments. Ultimately, this research will bring us closer to the dream of truly autonomous robots; robots that can interact with people and operate successfully in the complex environments we live in.This project also includes teaching efforts and the involvement of undergraduate students in research. Furthermore, it contains collaboration with an existing NSF project to expose young African-American students to the educational and career opportunities available in computer science, robotics and artificial intelligence.