CAREER: Towards a Theory of Robust Learning & Control for Safety-Critical Autonomous Systems

未来的自主系统，如自动驾驶汽车和灵活的机器人，将利用从复杂、高维、丰富的感知模式(如相机)收集的信息，在不断演变和不确定的条件下执行复杂和复杂的任务。由于这种无处不在的不确定性和复杂性，反馈控制回路在自治系统中已经并将继续普遍存在。经典的控制理论技术需要复杂和详细的动态系统模型，并假设信息由简单的单输出传感设备(如加速计)提供，这一假设显然不适用于未来自治系统的设想。相反，尽管机器和强化学习技术可以适应不确定的动态条件和丰富的感知模式，但它们往往要么只关注性能而忽略安全/健壮性问题，要么如果它们确实解决了安全问题，则仅适用于有限类别的系统。尽管最近在学习和控制的原则性集成方面取得了进展，但在可以被认证为安全、健壮和高性能的系统类别与真实世界的自治系统之间仍然存在着巨大的差距。该项目旨在制定一项研究计划，为稳健学习和稳健控制的新理论奠定基础，同时解决广泛的安全关键现实世界自治系统的安全和性能挑战。该项目的研究成果将被纳入协同教育计划，其中包括在宾夕法尼亚大学开发研究生和本科课程，旨在丰富向工程师教授自主性和控制系统的课程。宾夕法尼亚大学内公开可用的教育平台和推广计划也将被用来为STEM专业的学生建立进入大学的渠道，增加在代表不足的少数族裔中的代表性，促进围绕自主和控制系统的公共沟通，并传播研究成果。这项提议认为，需要一种关于稳健控制和稳健机器学习的新的跨学科视角，以在安全关键的复杂、动态和不确定的场景中释放基于学习的控制的真正潜力。Thrusts将开发新的基于学习的健壮控制策略，明确描述学习和控制管道中的不确定性的影响，并说明这些影响。第一个重点是学习使用当代的大容量模型，如深度神经网络，通过鲁棒学习和鲁棒控制技术的协同集成来控制未知的动态系统，旨在缓解分布偏移对闭环系统性能的有害影响。第二个重点是将控制理论的稳健性和稳定性保证扩展到具有复杂的高维传感模式的系统，如摄像机，通过开发工具，允许将这种复杂的感知传感器抽象地视为符合传统鲁棒控制方法的“噪声虚拟传感器”。最后，以基于感知的控制为例，研究了学习控制器的鲁棒性和样本复杂性的基本界限。因此，通过控制理论、机器与强化学习、统计学习理论和稳健优化工具的跨学科组合，该项目将开发出新的广泛适用的联合稳健学习和稳健控制工具，这些工具具有强大的性能、稳健性、安全性和样本效率保证。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。

Future autonomous systems, such as self-driving cars and agile robots, will be tasked with performing sophisticated and complex tasks under continuously evolving and uncertain conditions, using information gleaned from complex, high-dimensional, rich perceptual sensing modalities (e.g., cameras). Due to this ubiquitous uncertainty and complexity, feedback control loops are and will continue to be pervasive in autonomous systems. Classical control theory techniques require intricate and detailed models of dynamical systems, and assume that information is provided by simple, single output sensing devices (e.g., accelerometers), assumptions that clearly fail in the scenarios envisioned for future autonomous systems. Conversely, while techniques from machine and reinforcement learning can accommodate both uncertain dynamic conditions and rich perceptual sensing modalities, they tend to either focus solely on performance and ignore safety/robustness concerns, or if they do address safety, are only applicable to a limited class of systems. Despite recent progress in principled integration of learning and control, there still exists a wide gap between the class of systems that can be certified as safe, robust, and high-performing, and real-world autonomous systems. This project aims to develop a research plan that builds the foundations of a novel theory of robust learning and robust control that simultaneously addresses the challenges of safety and performance across a wide range of safety-critical real-world autonomous systems. The research outcomes of this project will be integrated into a synergistic education plan which includes developing both graduate and undergraduate courses at the University of Pennsylvania aimed at enriching the curriculum for teaching autonomy and control systems to engineers. Publicly available education platforms and outreach programs within the University of Pennsylvania will also be leveraged to build a pipeline for STEM majors entering college, to increase representation among underrepresented minorities, to advance public communication around autonomy and control systems, and to disseminate research results.This proposal argues that a novel cross-disciplinary perspective on robust control and robust machine learning is required to unlock the true potential of learning-based control in safety-critical complex, dynamic, and uncertain scenarios. Thrusts will develop novel robust learning-based control strategies that explicitly characterize and account for the effects of uncertainty in the learning and control pipeline. The first thrust focuses on learning to control an unknown dynamical system using contemporary high-capacity models, such as deep neural networks, through a synergistic integration of robust learning and robust control techniques aimed at mitigating the deleterious effects of distribution shift on closed-loop performance. The second thrust focuses on extending the robustness and stability guarantees of control theory to systems with complex, high-dimensional sensing modalities such as cameras, by developing tools that allow for such complex perceptual sensors to be abstractly viewed as “noisy-virtual sensors” that are amenable to traditional robust control methods. Finally, the third thrust initiates a study of the fundamental limits of the robustness and sample-complexity of learning-enabled controllers, using perception-based control as a case study. Thus, through an interdisciplinary mix of tools from control theory, machine & reinforcement learning, statistical learning theory, and robust optimization, this project will develop novel broadly applicable joint robust learning and robust control tools that come with strong guarantees of performance, robustness, safety, and sample-efficiency.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.