17 citations · 28 across the 11 of their papers we have counts for
13 papers
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…
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…
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Venkatraman Renganathan, Sleiman Safaoui, Aadi Kothari +3
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and predict…
Identification of Linear Systems with Multiplicative Noise from Multiple Trajectory Data
Yu Xing, Benjamin Gravell, Xingkang He +2
The paper studies identification of linear systems with multiplicative noise from multiple-trajectory data. An algorithm based on the least-squares method and multiple-trajectory d…
Anomaly Detection Under Multiplicative Noise Model Uncertainty
Venkatraman Renganathan, Benjamin J. Gravell, Justin Ruths +1
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are susceptible to model risk as…
Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty
Sleiman Safaoui, Benjamin J. Gravell, Venkatraman Renganathan +1
We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT* (RANS-RRT*) as an RRT* variant…
Centralized Collision-free Polynomial Trajectories and Goal Assignment for Aerial Swarms
Benjamin Gravell, Tyler Summers
Computationally tractable methods are developed for centralized goal assignment and planning of collision-free polynomial-in-time trajectories for systems of multiple aerial robots…
Approximate Midpoint Policy Iteration for Linear Quadratic Control
Benjamin Gravell, Iman Shames, Tyler Summers
We present a midpoint policy iteration algorithm to solve linear quadratic optimal control problems in both model-based and model-free settings. The algorithm is a variation of New…
Robust Control Design for Linear Systems via Multiplicative Noise
Benjamin Gravell, Peyman Mohajerin Esfahani, Tyler Summers
Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for di…
Robust Learning-Based Control via Bootstrapped Multiplicative Noise
Benjamin Gravell, Tyler Summers
Despite decades of research and recent progress in adaptive control and reinforcement learning, there remains a fundamental lack of understanding in designing controllers that prov…
Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data
Yu Xing, Ben Gravell, Xingkang He +2
The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control…
Sparse optimal control of networks with multiplicative noise via policy gradient
Benjamin Gravell, Yi Guo, Tyler Summers
We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasin…
Learning robust control for LQR systems with multiplicative noise via policy gradient
Benjamin Gravell, Peyman Mohajerin Esfahani, Tyler Summers
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuo…