权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Generalization and Safety Guarantees for Learning-Based Control of Robots

职业：基于学习的机器人控制的泛化和安全保证

基本信息

批准号：
2044149
负责人：
Anirudha Majumdar
金额：
$ 54.6万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044149&HistoricalAwards=false
关键词：
CAREER Generalization Safety Guarantees Learning

项目摘要

The ability of machine learning techniques to process rich sensory inputs such as vision makes them highly appealing for use in robotic systems (e.g., micro aerial vehicles and robotic manipulators). However, the increasing adoption of learning-based components in the robotics perception and control pipeline poses an important challenge: how can we guarantee the safety and performance of such systems? As an example, consider a micro aerial vehicle that learns to navigate using a thousand different obstacle environments or a robotic manipulator that learns to grasp using a million objects in a dataset. How likely are these systems to remain safe and perform well on a novel (i.e., previously unseen) environment or object? How can we learn control policies for robotic systems that provably generalize well to environments that our robot has not previously encountered? Unfortunately, existing approaches either do not provide such guarantees or do so only under very restrictive assumptions. This Faculty Early Career Development (CAREER) project seeks to establish a foundational framework for learning-based control of safety-critical robotic systems with guaranteed generalization and safety. The project will impact challenging application domains such as aerial inspection and manipulation (e.g., for infrastructure repair tasks) and includes activities for (i) engaging regulatory agencies and industry entities in discussions regarding the certification of learning-based robotic systems, (ii) partnering with teacher preparation programs and other educational programs to engage high-school and undergraduate students in robotics, and (iii) widely disseminating materials from a new robotics course which uses hands-on labs with drones. Motivated by the need for guaranteeing the safety of learning-based robotic systems, this project is developing a principled theoretical and algorithmic framework for learning control policies for robotic systems with provable guarantees on generalization to novel environments (i.e., environments that the robot has not previously encountered). The key technical insight of this project is to leverage and extend powerful techniques from generalization theory in theoretical machine learning. The resulting framework provides bounds on the expected performance of learned policies (including ones based on neural networks) across novel environments. The project is developing algorithms (based on convex optimization, gradient-based methods, and black-box optimization) for learning policies that explicitly optimize these bounds. The project also seeks to guarantee the robustness of learned policies to shifts in the distribution of environments that the robot encounters. An important part of the effort is to thoroughly validate the technical approach on hardware platforms including micro aerial vehicles performing navigation, inspection, and aerial manipulation tasks motivated by infrastructure repair applications. 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.

机器学习技术处理视觉等丰富的感觉输入的能力使其在机器人系统(如微型飞行器和机器人操作器)中的使用具有极大的吸引力。然而，在机器人感知和控制管道中越来越多地采用基于学习的组件构成了一个重要的挑战：我们如何保证此类系统的安全和性能？作为一个例子，假设一个微型飞行器使用一千个不同的障碍环境学习导航，或者一个机器人机械手使用数据集中的一百万个对象学习抓取。这些系统在新的(即以前看不见的)环境或物体上保持安全并良好运行的可能性有多大？我们如何学习机器人系统的控制策略，这些策略可以很好地推广到我们的机器人以前从未遇到过的环境中？不幸的是，现有的方法要么不提供这种保证，要么只在非常有限的假设下这样做。该学院早期职业发展(CAREAGE)项目旨在为安全关键机器人系统的基于学习的控制建立一个基本框架，并保证通用性和安全性。该项目将影响具有挑战性的应用领域，如空中检查和操纵(例如，基础设施维修任务)，并包括以下活动：(I)让监管机构和行业实体参与关于基于学习的机器人系统认证的讨论，(Ii)与教师培训计划和其他教育计划合作，让高中生和本科生学习机器人技术，以及(Iii)广泛传播使用无人机动手实验室的新机器人课程的材料。出于保证基于学习的机器人系统安全的需要，该项目正在为机器人系统的学习控制政策制定一个原则性的理论和算法框架，并对推广到新环境(即机器人以前从未遇到过的环境)提供可证明的保证。该项目的关键技术见解是利用和扩展理论机器学习中泛化理论的强大技术。由此产生的框架为跨新环境的学习策略(包括基于神经网络的策略)的预期性能提供了界限。该项目正在开发用于明确优化这些界限的学习策略的算法(基于凸优化、基于梯度的方法和黑盒优化)。该项目还寻求确保学习的策略对机器人遇到的环境分布变化的稳健性。这项工作的一个重要部分是在硬件平台上彻底验证技术方法，包括执行基础设施修复应用程序所驱动的导航、检查和空中操纵任务的微型飞行器。该项目由跨部门机器人基础研究计划支持，该计划由工程学指导委员会(ENG)和计算机与信息科学与工程指导委员会(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。