CAREER: Human-Inspired Multi-Robot Navigation

职业：受人类启发的多机器人导航

• 批准号: 2402338
• 负责人: Ioannis Karamouzas
• 金额: $ 50.18万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2402338&HistoricalAwards=false
• 关键词: CAREER; Human; Inspired; Multi; Robot

Indoor mobile robots are increasingly becoming a part of our lives. Whether there are Roombas cleaning the floor or Kiva robots delivering parts in warehouses, the robots should be able to avoid collisions while successfully completing their tasks. However, despite the maturity of existing motion planning techniques and the recent rise of learning and big data techniques, mobile robots still lack the decision making ability of humans. This Faculty Early Career Development (CAREER) project will develop techniques for efficient and socially intelligent multi-robot navigation, shaping the next generation of mobile robots that can reason about how their actions influence the other agents present in the scene and act accordingly, much like humans do. The resulting advances will facilitate the successful deployment of "thinking" mobile robots that can be seamlessly integrated into our homes and workspaces. This research spans across different areas, including motion planning, machine learning, and reinforcement learning. With its interdisciplinary nature and relevance for modern technologies, it is ideal for inspiring the next generation of students and exposing the broader community to STEM areas couched in progressive applications in robotics and AI. The project includes integrated educational, research, and outreach activities for K-12, undergraduate, and graduate students, promoting a high level of participation by women and underrepresented minorities, and developing new courses and updated curricula related to robotics.This project will introduce a human-inspired paradigm shift in the design of multi-robot navigation algorithms. Humans know when they have to be polite and yield to others and when to take decisive actions, efficiently performing complex navigation tasks without collisions. The objective of this project is to enable such behavior on mobile robots by leveraging publicly available human-human interaction data and our own human-robot interaction experiments along with coupling motion planning with learning techniques. Specifically, the project will focus on two two inter-related research thrusts that will lead to i) new algorithms that take advantage of human trajectory datasets to learn what controls humans take in different interaction scenarios; ii) new approaches that enhance existing local navigation planners with the learned controls to enable human-like decision making; iii) a reinforcement learning framework for multi-robot navigation that generalizes robot navigation policies to unknown interactions scenarios; iv) new datasets involving interactions between humans and robots, and subsequently v) new algorithms for multi-robot navigation in human-populated environments. This work will be evaluated both in simulation and on real robots, and related algorithms and datasets will be made publicly available to facilitate further research and exploration by the robotics and AI community. If successful, this project will shape the next generation of indoor mobile robots that can enrich our quality of life and work, and has the potential to significantly benefit society through its integrated education plan.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). This project is jointly funded by CISE/IIS, the Established Program to Stimulate Competitive Research (EPSCoR), and ENG/CMMI.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

室内移动机器人越来越多地成为我们生活的一部分。无论是有室内室清洁地板还是在仓库中运送零件的Kiva机器人，机器人都应该能够在成功完成任务的同时避免碰撞。但是，尽管现有运动计划技术的成熟以及最近的学习和大数据技术的兴起，但移动机器人仍然缺乏人类的决策能力。这个教师的早期职业发展（职业）项目将开发技术，以提高效率和社会智能的多机器人导航，塑造下一代移动机器人，这些机器人可以推理其行为如何影响现场中的其他特工并像人类一样采取相应的行动。最终的进步将有助于成功地部署“思考”移动机器人，这些机器人可以无缝集成到我们的房屋和工作区中。这项研究跨越了不同领域，包括运动计划，机器学习和增强学习。它具有跨学科的性质和与现代技术的相关性，非常适合激发下一代学生并将更广泛的社区暴露于机器人技术和AI中渐进式应用中的STEM区域。该项目包括针对K-12，本科生和研究生的综合教育，研究和外展活动，促进妇女和人为少数群体的高度参与，并开发与Robotics相关的新课程和更新的课程。该项目将引入人力启发的范式在人力范围内的范式转移多人 - 竞争alg-Robot Algorith Algorith Algorith algoRith algorith algoRith algorith algorith algorith algoRithms。人类知道何时必须礼貌并屈服于他人，以及何时采取决定性的行动，在没有碰撞的情况下有效执行复杂的导航任务。该项目的目的是通过利用公开可用的人类互动数据以及我们自己的人类机器人互动实验以及与学习技术的耦合运动计划来实现移动机器人的这种行为。具体而言，该项目将集中在两个相互关联的研究推力上，这将导致i）利用人类轨迹数据集的新算法来了解人类在不同的互动场景中所采取的控制； ii）使用学识渊博的控件增强现有的本地导航计划者的新方法，以实现类似人类的决策； iii）多机器人导航的增强学习框架，将机器人导航策略推广到未知的互动场景； iv）涉及人与机器人之间相互作用的新数据集，随后v）在人填充的环境中用于多机器人导航的新算法。这项工作将在模拟和真实机器人中进行评估，相关算法和数据集将公开使用，以促进机器人和AI社区的进一步研究和探索。如果成功的话，该项目将塑造下一代室内移动机器人，可以丰富我们的生活质量和工作，并有可能通过其综合教育计划使社会受益匪浅。该项目得到了机器人计划中的跨导向基础研究的支持，该项目由机器人计划中的跨领域基础研究（由工程和计算机以及计算机和信息科学和工程（CISE）共同管理和资助。该项目由CISE/IIS共同资助，该项目刺激了竞争研究（EPSCOR），ENG/CMMI。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子评估来获得支持的，并具有更广泛的影响。