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

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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.