EAGER: Human-Aware Navigation in Populated Indoor Environments

EAGER：人口稠密的室内环境中的人类感知导航

• 批准号: 1651089
• 负责人: Peter Stone
• 金额: $ 25.94万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651089&HistoricalAwards=false
• 关键词: EAGER; Human; Aware; Navigation; Populated

Current autonomous mobile robots are able to navigate accurately through sparsely populated areas without bumping into things. However, they have more trouble in situations that commonly arise in public buildings, such as when passing people in narrow hallways, when moving through open, populated spaces, or when crossing a crowd of people exiting an auditorium. Thus, for autonomous robots to reach their full potential, in terms of positive impact on society, they will need to improve their navigational abilities to be more "human-aware." That is, they will explicitly need to take account the characteristics of the people with whom they need to interact in public spaces. With this motivation in mind, the goal of this research is to understand how best to enable mobile robots to navigate smoothly, robustly, and safely through human-populated indoor environments in pursuit of high-level goals, with varying levels of guidance from a human operator in a fully human-aware manner.This project focuses on two complementary, high-risk, and potentially foundational research thrusts as being crucial to laying the groundwork for eventual development of a robust, human-aware navigation system. First, it aims to develop formal specifications for safe robot-operator-pedestrian interactions, using probabilistic temporal logics. Second, it aims to develop methods for generating learned models of operator preferences that can influence the robot's choice of paths with regards to, for example, trajectory smoothness, order of subgoal achievement, task completion time, travel speed, and proximity of trajectory to pedestrians and fixed objects, learning user preferences and determining how to combine them with task-achieving reward functions.

目前的自主移动的机器人能够在人口稀少的地区准确导航而不会撞到东西。 然而，在公共建筑中通常出现的情况下，他们会遇到更多的麻烦，例如在狭窄的走廊中经过人群时，穿过开放的人口密集的空间时，或者穿过人群离开礼堂时。 因此，为了让自主机器人充分发挥其潜力，在对社会产生积极影响方面，它们需要提高导航能力，使其更具“人类意识”。“也就是说，他们将明确需要考虑到他们需要在公共空间与之互动的人的特点。 考虑到这一动机，本研究的目标是了解如何最好地使移动的机器人能够在追求高水平目标的过程中平稳，稳健，安全地通过人类居住的室内环境，并以完全人类感知的方式从人类操作员那里获得不同程度的指导。和潜在的基础研究推力，为最终开发一个强大的，人类感知的导航系统奠定基础至关重要。 首先，它的目的是开发安全的机器人操作员行人互动的正式规范，使用概率时间逻辑。 其次，它的目的是开发方法，用于生成学习模型的操作员的喜好，可以影响机器人的路径选择方面，例如，轨迹平滑度，子目标的实现，任务完成时间，行驶速度，以及接近的轨迹行人和固定物体，学习用户的喜好，并确定如何将它们与任务实现奖励功能联合收割机。

项目成果

Learning from Demonstrations with High-Level Side Information

从具有高级辅助信息的演示中学习

• 发表时间: 2017
• 期刊: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
• 作者: Wen, Min;Papusha, Ivan;Topcu, Ufuk
• 通讯作者: Topcu, Ufuk