CAREER: Intelligent Manipulation in the Real World via Modularity and Abstraction

该学院早期职业发展(Career)项目旨在使智能机器人能够在现实世界的任务中看到、思考和行动。近年来，机器人在深度学习方面取得了长足的进步。然而，最先进的操纵算法仍然缺乏普适性和健壮性，无法广泛应用。它对从工业自动化到通用家用机器人的现实世界应用构成了主要障碍。这项研究将加快熟练和可靠的机器人从研究环境到现实世界环境的部署。它将为变革性机器人技术在实际应用中做出根本性贡献，包括制造、医疗保健和家庭辅助。作为该项目的一部分，教育计划将通过课程开发和研究指导将这项研究整合到本科和研究生教育中。外展活动将包括一项开源软件倡议和针对高中生的K-12教育计划。这个项目的总体目标是为现实世界中的智能机器人操作建立新的算法和工具。这项研究的关键是一套算法，可以构建有效的抽象并利用这些抽象来合成复杂的行为。具体地说，本研究有三个密切相关的研究主题：1)用以对象为中心的表征发展知觉抽象，2)用感觉运动技能建立运动抽象，3)用这些抽象来模拟复杂的操作任务。算法将在模块和系统级别进行严格评估，并部署在物理机器人硬件上，以执行一系列家庭任务。该项目由跨部门机器人基础研究计划支持，该计划由工程指导委员会(ENG)和计算机和信息科学与工程指导委员会(CEISE)联合管理和资助。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。

This Faculty Early Career Development (CAREER) project seeks to enable intelligent robots to see, think, and act in real-world tasks. Recent years have witnessed great strides in deep learning for robotics. Yet, state-of-the-art manipulation algorithms still fall short of generalization and robustness for widespread deployment. It poses a major obstacle to real-world applications from industrial automation to general-purpose household robots. This research will accelerate the deployment of adept and reliable robots from research settings to real-world environments. It will make fundamental contributions to transformative robotics technologies in practical applications, including manufacturing, healthcare, and home assistance. As part of the project, the education plan will integrate this research into undergraduate and graduate education through course development and research mentorship. The outreach activities will include an open-source software initiative and K-12 educational programs for high school students. The overall objective of this project is to build new algorithms and tools for intelligent robot manipulation in the real world. The crux of this research is a suite of algorithms that build effective abstractions and harness these abstractions to synthesize sophisticated behaviors. Concretely, the research has three intimately connected research themes: 1) developing perceptual abstraction with object-centric representations, 2) building motor abstraction with sensorimotor skills, and 3) modeling complex manipulation tasks with these abstractions. The algorithms will be rigorously evaluated at the module and system levels and deployed on the physical robot hardware to perform a spectrum of household tasks.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.