In many modern engineering domains, the presence of "observer effects" creates interdependence between measurement and underlying state. In such settings, control actions  both impact the system state and determine what information about it is observed. Accounting for this dual role is crucial for designing reliable algorithms for learning and control, for applications ranging from robotics to personalized recommendation systems. In this talk, I will discuss recent work in the setting of partially observed dynamical systems with linear state transitions and bilinear observations. Inspired by the rich line of work on learning and control for linear systems, our goal is to understand how much (and which) data is necessary for reliable decision-making.

First, I will discuss learning from observations when the dynamics are unknown and provide finite data error bounds and a sample complexity analysis for inputs chosen according to a simple random design. Second, we will consider the optimal control problem with the objective of minimizing a quadratic cost. Despite the similarity to standard linear quadratic Gaussian (LQG) control, neither does the separation principle (SP) hold, nor is the optimal policy affine in the estimated state. Under certain conditions, the SP-based controller locally maximizes the cost instead of minimizing it, and instability can result from a loss of observability. By accounting for how the actions impact state estimation, I will introduce an MPC controller based on receding horizon planning in the belief space. I will conclude with a discussion of open questions on control design and end-to-end guarantees. Based on joint work with Yahya Sattar, Sunmook Choi, Yassir Jedra, Leo Maynard-Zhang, and Maryam Fazel

 

Sarah Dean is an assistant professor of computer science. She studies the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on understanding the fundamentals of data-driven methods for control and decision-making, inspired by applications ranging from robotics to recommendation systems. She completed her postdoctoral research at the University of Washington and earned her M.S. and Ph.D. in electrical engineering and computer science at the University of California, Berkeley. Dean received her B.S.E. in electrical engineering and mathematics from the University of Pennsylvania.