21 citations · 24 across the 12 of their papers we have counts for
7 papers · 1 filter
Data-driven distributionally robust MPC for systems with uncertain dynamics
Francesco Micheli, Tyler Summers, John Lygeros
We present a novel data-driven distributionally robust Model Predictive Control formulation for unknown discrete-time linear time-invariant systems affected by unknown and possibly…
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
Benjamin Gravell, Iman Shames, Tyler Summers
We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design. The algo…
Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic Systems
Sleiman Safaoui, Lars Lindemann, Iman Shames +1
Motivated by the recent interest in risk-aware control, we study a continuous-time control synthesis problem to bound the risk that a stochastic linear system violates a given spec…
Policy Iteration for Multiplicative Noise Output Feedback Control
Benjamin Gravell, Matilde Gargiani, John Lygeros +1
We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem. The algorithm solves a set of coupled Riccati equation…
Probabilistic Data Association for Semantic SLAM at Scale
Elad Michael, Tyler Summers, Tony A. Wood +2
With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Pre…
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli +2
Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance…