RI-Small: Statistical Relational Models for Semantic Robot Mapping

我们如何才能制造出能够区分和处理日常环境中许多物体的机器人？ 我们如何赋予这些机器人对空间概念（如房间、走廊、街道和十字路口）进行推理的能力？ 尽管机器人社区在开发用于表示和处理噪声传感器信息的有效技术方面取得了巨大进展，但当前的技术并不具有解决这些问题的表达能力。在这个项目中，我们将开发统计关系机器学习技术，能够从机器人传感器数据中提取高级概念。 通过转移在其他环境中学到的知识，我们的技术将使机器人能够识别以前看不到的环境中的物体和地点。 最终，这项研究将使我们更接近真正自主机器人的梦想;机器人可以与人互动，并在我们生活的复杂环境中成功运作。 此外，它还包括与现有的NSF项目合作，使年轻的非洲裔美国学生接触计算机科学，机器人和人工智能方面的教育和职业机会。

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.