CAREER: Cooperative Motion Planning for Human-Operated Robots

职业：人类操作机器人的协作运动规划

• 批准号: 1503177
• 负责人: Kris Hauser
• 金额: $ 37.85万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1503177&HistoricalAwards=false
• 关键词: CAREER; Cooperative; Motion; Planning; Human

This proposal outlines a research and educational plan to advance decision-making techniques for robots that cooperate with human operators. Because humans far exceed the abilities of state-of-the-art robots in vision, creativity, and adaptability, interest is rapidly growing in a human-centered approach to robotics: combining the strengths of humans with the superior precision and repeatability of robots. And yet, our available motion planning tools, while powerful at computing motions for complex autonomous tasks, are poorly suited for human-centered applications that demand responsive and natural motions. This proposal hypothesizes that a new cooperative motion planning paradigm will support major advances in intuitiveness and task performance of human-operated robots such as intelligent vehicles, tele-surgery systems, search and-rescue robots, and household robots. This hypothesis is echoed in an educational plan that aims to train engineers with cross-disciplinary strengths that bridge both the technical and social dimensions of robotics. Initial human subjects studies on novice operators with the PI's cooperative motion planning algorithms suggest that the technique leads to dramatic reductions in task completion time and collision rate in cluttered environments. The proposed work will conduct further investigations along this line of research to 1) identify characteristics of cooperative planners - such as optimality, responsiveness, and completeness - that yield effective human-operator systems, both in terms of objective performance metrics and subjective preferences, 2) to design planners that optimize cooperativity metrics under computational resource and communication constraints, and 3) to enhance the capabilities of such planners to assist operators in complex manipulation tasks.The planners developed in this research and the rich datasets acquired via user studies will serve as resources to help human-robot interaction (HRI) researchers design safe and socially acceptable robot behaviors. Moreover, advances in cooperative motion planning may have long-term social and economic impact by enabling new applications of robotics in driver assist systems, space exploration, medicine, household robotics, manufacturing, and construction. Research is integrated with education in a range of activities that include CS curriculum development, development of a new graduate course on optimization and machine learning, and in new software libraries for robotics education. New modules on motion planning, behavior recognition, and HRI will be incorporated in AI and robotics courses. An REU is requested for each summer of the grant and will be recruited from a minority-serving institution in cooperation with the Alliance for the Advancement of African-American Researchers in Computing (A4RC). One or more IU undergraduates will be involved in research and mentored according to the Undergraduate Research Opportunities in Computing (UROC) program, with preference given to minority and women students.

该提案概述了一项研究和教育计划，以推进与人类操作员合作的机器人的决策技术。由于人类在视觉、创造力和适应性方面远远超过了最先进的机器人的能力，因此人们对以人为中心的机器人方法的兴趣正在迅速增长：将人类的优势与机器人的卓越精度和可重复性相结合。然而，我们可用的运动规划工具虽然在计算复杂自主任务的运动方面功能强大，但不太适合需要响应性和自然运动的以人为中心的应用程序。该提案假设，新的协作运动规划范式将支持人类操作机器人（例如智能车辆、远程手术系统、搜索救援机器人和家用机器人）在直观性和任务性能方面的重大进步。这一假设在一项教育计划中得到了呼应，该计划旨在培养具有跨学科优势的工程师，从而在机器人技术和社会层面之间架起桥梁。对新手操作员使用 PI 协作运动规划算法进行的初步人类受试者研究表明，该技术可大幅减少杂乱环境中的任务完成时间和碰撞率。拟议的工作将沿着这一研究方向进行进一步的调查，以 1）确定合作规划者的特征 - 例如最优性、响应性和完整性 - 从而产生有效的人类操作员系统，无论是在客观绩效指标还是主观偏好方面，2 ）设计在计算资源和通信限制下优化协作性指标的规划器，以及3）增强此类规划器协助操作员完成复杂操作任务的能力。本研究中开发的规划器以及通过用户研究获得的丰富数据集将作为帮助人机交互（HRI）研究人员设计安全且社会可接受的机器人行为的资源。此外，协作运动规划的进步可能会通过在驾驶员辅助系统、太空探索、医学、家用机器人、制造和建筑领域实现机器人技术的新应用而产生长期的社会和经济影响。研究与一系列活动的教育相结合，包括计算机科学课程开发、优化和机器学习新研究生课程的开发，以及用于机器人教育的新软件库。有关运动规划、行为识别和 HRI 的新模块将纳入人工智能和机器人课程。每年夏天都会申请一名 REU，并将与非裔美国计算机研究人员促进联盟 (A4RC) 合作，从一家为少数族裔服务的机构中招募一名 REU。一名或多名印第安纳大学本科生将根据计算机本科生研究机会 (UROC) 计划参与研究并接受指导，优先考虑少数族裔和女学生。