NRI: Collaborative Goal and Policy Learning from Human Operators of Construction Co¬-Robots

NRI：建筑协作机器人操作员的协作目标和政策学习

• 批准号: 1719291
• 负责人: Girish Chowdhary
• 金额: $ 70.32万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719291&HistoricalAwards=false
• 关键词: NRI; Collaborative; Goal; Policy; Learning

The overall goal of this research is to investigate and significantly advance the science of collaborative interaction between human operators and co-robots. This includes the development of algorithms that can be used to train co-robots from skilled human operators to efficiently perform complex tasks in the face of real-world uncertainty, and to guide novice operators in performing such tasks. The primary targeted application is the construction and farming equipment industry that includes complex co-robots such as excavators, wheel loaders, tractors, forage harvesters where there is a significant need to understand and improve human-robot collaborative learning.There are significant scientific challenges in developing efficient algorithms for co-robots that can actively learn from skilled human operators by observing and posing appropriate queries to close the feedback loop between the co-robot and the human operator. This project addresses these challenges by systematically formulating and investigating focused problems to create efficient algorithms that can enhance collaborative human-robot learning. To achieve the goal, algorithms are designed to collaboratively learn latent subgoal structures from ill-defined complex tasks, real-time path planning and control algorithms are developed for co-robots to achieve the learned subgoals, and techniques are developed to provide operator skill specific task decomposition and motion execution guidance. In addition, the developed algorithms are corroborated by simulators, hardware experimentation on laboratory and field co-robots, and theoretical analysis.

这项研究的总体目标是调查和显着推进人类操作员和合作机器人之间的协作交互科学。这包括开发可用于训练熟练人类操作员的协作机器人的算法，以在现实世界的不确定性面前有效地执行复杂任务，并指导新手操作员执行这些任务。主要目标应用是建筑和农业设备行业，包括复杂的协作机器人，如挖掘机，轮式装载机，拖拉机，饲料收割机，其中有一个显着的需要，以了解和改善人机协作学习。有显着的科学挑战，在开发有效的算法，机器人可以通过观察和提出适当的查询来主动地向熟练的人类操作员学习，以关闭合作机器人和人类操作员之间的反馈回路。该项目通过系统地制定和研究重点问题来解决这些挑战，以创建可以增强人机协作学习的高效算法。为了实现这一目标，算法的目的是协同学习潜在的子目标结构，从定义不清的复杂任务，实时路径规划和控制算法的开发合作机器人，以实现学习的子目标，和技术的开发，以提供操作员技能特定的任务分解和运动执行指导。此外，所开发的算法证实了模拟器，实验室和现场合作机器人的硬件实验，和理论分析。