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成为我们生活的一部分。无论是 Roombas 清洁地板还是 Kiva 机器人在仓库中运送零件，机器人都应该能够在成功完成任务的同时避免碰撞。然而，尽管现有的运动规划技术已经成熟，并且近年来学习和大数据技术的兴起，移动机器人仍然缺乏人类的决策能力。该学院早期职业发展（CAREER）项目将开发高效且社交智能的多机器人导航技术，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场景中存在的其他代理并采取相应的行动，就像人类确实如此。由此产生的进步将有助于成功部署“会思考”的移动机器人，这些机器人可以无缝集成到我们的家庭和工作场所中。这项研究跨越不同领域，包括运动规划、机器学习和强化学习。凭借其跨学科性质和与现代技术的相关性，它非常适合激励下一代学生，并使更广泛的社区接触机器人和人工智能先进应用中的 STEM 领域。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，促进女性和代表性不足的少数族裔的高水平参与，并开发与机器人相关的新课程和更新课程。该项目将引入多机器人导航算法设计中受人类启发的范式转变。人类知道何时必须礼貌并屈服于他人，何时采取果断行动，从而有效地执行复杂的导航任务而不会发生碰撞。该项目的目标是通过利用公开的人机交互数据和我们自己的人机交互实验以及将运动规划与学习技术相结合，在移动机器人上实现这种行为。具体来说，该项目将重点关注两个两个相互关联的研究重点，这将导致 i）利用人类轨迹数据集来了解人类在不同交互场景中采取哪些控制的新算法； ii) 通过学习控制来增强现有本地导航规划器的新方法，以实现类人决策； iii）用于多机器人导航的强化学习框架，将机器人导航策略推广到未知的交互场景； iv）涉及人类和机器人之间交互的新数据集，以及随后的 v）用于人类居住环境中的多机器人导航的新算法。这项工作将在模拟和真实机器人上进行评估，相关算法和数据集将公开，以促进机器人和人工智能社区的进一步研究和探索。如果成功，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和工作质量，并有潜力通过其综合教育计划为社会带来显着造福。该项目得到了跨部门基础研究的支持机器人项目，由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (CISE) 共同管理和资助。该项目由 CISE/IIS、刺激竞争研究既定计划 (EPSCoR) 和 ENG/CMMI 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。