CAREER: Re-Thinking the Perception-Action Paradigm for Agile Autonomous Robots

自主机器人将在我们的社会中变得无处不在，并将解决复杂的任务，相互之间以及与人类积极合作。正如最近的新冠肺炎疫情所突显的那样，自主机器人可以解决一系列时间敏感的问题，包括物流、侦察和关键区域的消毒。除了流行病，小型机器人还可以帮助人类执行复杂或危险的任务，如搜救、安全和监视，而且由于重量较轻，它们对人类安全只构成适度的风险。这些时间敏感的任务要求机器人在复杂和动态的环境中做出快速决策和灵活的机动。最先进的自主导航方法虽然成熟，但速度慢且脆弱，阻碍了稳健和弹性的灵活导航。该学院早期职业发展(Career)计划通过规划一种新颖、低延迟、健壮、自适应、安全和弹性的范例，研究复杂环境中自主机器人灵活导航的基本感知-行动问题。该项目还旨在通过建立一个独特的多学科和包容性研究和教育平台来教育学生关于自主系统的技术方面、社会效益和道德使用，其中包括关于机器人本地化和导航的核心课程，以及一系列在线赛车黑客松，以获得疫情后定制和包容性研究和教育体验。这些将有助于降低学生，特别是代表性不足的少数民族参与研究和教育的门槛。该项目将产生一个新的基础理论，其中包括感知、学习和控制的原则性组合产生的模型和算法，以整体设计视觉感知和行动，以创建小规模灵活的自主机器人。其目标是捕捉感知和动作之间的严格交叉耦合效应，共同并行地解决感知-动作问题，以加快机器人的决策过程，增加其敏捷性。该项目根据一系列目标分三个阶段进行组织，最终在机器人自主研究和教育方面取得创新。感知空间和动作空间的压缩和统一表示保证了机器人的推理延迟，并自然地揭示了它们之间的交叉耦合效应。接下来，机器人将利用其动作预测信息来增强其推理能力，并将采用最优控制/规划方法来最大限度地提高其感知精度和质量。该项目由跨部门机器人基础研究计划支持，该计划由工程总监(ENG)和计算机和信息科学与工程(CEISE)共同管理和资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Autonomous robots will become pervasive in our society and will solve complex tasks, actively collaborating with each other and with humans. As the recent COVID-19 outbreak has highlighted, autonomous robots can solve a range of time-sensitive problems including logistics, reconnaissance, and disinfection of critical areas. Beyond pandemic, small-scale robots can help humans in complex or dangerous tasks such as search and rescue, security, and surveillance, and, thanks to their lighter weight, they pose only a modest risk to human safety. These time-sensitive tasks require robots to make fast decisions and agile maneuvers in complex and dynamic environments. State-of-the-art autonomous navigation approaches, while mature, are slow and brittle and prevent robust and resilient agile navigation. This Faculty Early Career Development (CAREER) Program studies the fundamental perception-action problem for agile navigation of autonomous robots in complex environments by planning a novel, low-latency, robust, adaptive, safe, and resilient paradigm. This project aims also to educate students on the technical aspects, societal benefits, and ethical use of autonomous systems by establishing a unique multi-disciplinary, and inclusive research and educational platform which includes a core curriculum on robot localization and navigation, and a series of online racing hackathons for a post-pandemic customized and inclusive research and educational experience. These will contribute to lowering the barrier to participation in research and education for students, particularly underrepresented minorities.This project will generate a new foundational theory, which includes models and algorithms resulting from a principled combination of perception, learning, and control to holistically design visual perception and action to create small-scale agile autonomous robots. The goal is to capture the strict cross–coupling effects between perception and action to jointly and concurrently resolve the perception-action problem to speed up the robots’ decision making process and increase their agility. The project is organized in three thrusts according to a series of objectives, culminating in innovations in robotics autonomy research and education. A compressed and unified representation of the perception and action spaces guarantees to reduce the robot's inference latency and naturally reveals the cross-coupling effects among them. Next, the robot will exploit using this representation its action-predictive information to enhance its inference capabilities and will employ an optimal control/planning approach to maximize its perception accuracy and quality.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 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.