CAREER: Cooperative Motion Planning for Human-Operated Robots

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最先进的机器人，因此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上级精度和可重复性相结合。然而，我们现有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，但不适合以人为本的应用程序，这些应用程序需要响应和自然的运动。该提案假设，一个新的合作运动规划范式将支持人类操作的机器人，如智能车辆，远程手术系统，搜索和救援机器人，家用机器人的直观性和任务性能的重大进展。这一假设在一项教育计划中得到了回应，该计划旨在培养具有跨学科优势的工程师，这些工程师将机器人技术和社会层面联系起来。初步人类受试者的研究与PI的合作运动规划算法的新手操作员表明，该技术导致显着减少任务完成时间和碰撞率在混乱的环境。拟议的工作将进行进一步的调查沿着这条线的研究，以1）确定合作规划师的特点-如最优性，响应性和完整性-产生有效的人类操作员系统，无论是在客观性能指标和主观偏好，2）设计规划师，优化计算资源和通信约束下的合作性指标，本研究所开发的规划器和通过用户研究获得的丰富数据集将作为资源，帮助人机交互（HRI）研究人员设计安全和社会可接受的机器人行为。此外，协作运动规划的进步可能会产生长期的社会和经济影响，使机器人技术在驾驶员辅助系统，太空探索，医学，家用机器人，制造业和建筑业的新应用。研究与教育相结合的一系列活动，包括CS课程开发，开发一个新的研究生课程优化和机器学习，并在新的软件库机器人教育。关于运动规划，行为识别和HRI的新模块将被纳入AI和机器人课程。每年夏季的赠款要求一个REU，并将从一个少数民族服务机构与促进非洲裔美国人计算研究人员联盟（A4 RC）合作招募。一个或多个IU本科生将参与研究，并根据计算机本科生研究机会（UROC）计划进行指导，优先考虑少数民族和女性学生。

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.