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

机器学习技术处理视觉等丰富感官输入的能力使其在机器人系统（例如微型飞行器和机器人操纵器）中的应用非常有吸引力。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提出了一个重要的挑战：我们如何保证这些系统的安全性和性能？举个例子，考虑一个微型飞行器，它可以使用一千种不同的障碍环境来学习导航，或者一个机器人操纵器，它可以使用数据集中的一百万个对象来学习抓取。这些系统在一个新的（例如，以前看不见的）环境或对象上保持安全并表现良好的可能性有多大？我们如何学习机器人系统的控制策略，证明它可以很好地推广到我们的机器人以前没有遇到过的环境？不幸的是，现有的方法要么不能提供这样的保证，要么只能在非常严格的假设下提供这样的保证。这个教师早期职业发展（Career）项目旨在建立一个基于学习的安全关键机器人系统控制的基础框架，保证其通用性和安全性。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，用于基础设施维修任务），并包括以下活动：(i)参与监管机构和行业实体讨论关于学习型机器人系统的认证，（ii）与教师准备计划和其他教育计划合作，吸引高中和本科生参与机器人技术。和（三）广泛传播材料从一个新的机器人课程，使用无人机动手实验室。由于需要保证基于学习的机器人系统的安全性，该项目正在开发一个原则性的理论和算法框架，用于机器人系统的学习控制策略，并具有可证明的保证，可以推广到新的环境（即机器人以前没有遇到过的环境）。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。由此产生的框架提供了在新环境中学习策略（包括基于神经网络的策略）的预期性能的界限。该项目正在开发算法（基于凸优化、基于梯度的方法和黑盒优化），用于明确优化这些边界的学习策略。该项目还试图保证学习策略对机器人遇到的环境分布变化的鲁棒性。工作的一个重要部分是在硬件平台上彻底验证技术方法，包括执行导航、检查和由基础设施维修应用驱动的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项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.