Tyler Westenbroek

I draw on tools from control theory, optimization and machine learning to develop scalable, robust and interpretable data-driven control techniques for complex real-world robotic systems. I am currently a Postdoctoral Researcher at the Oden Institute for Computational and Engineering Sciences at UT Austin, hosted by Ufuk Topcu and David Fridovich-Keil.

In Feb 2023, I received a PhD in Electrical Engineering and Computer Sciences from UC Berkeley under the supervision of Shankar Sastry. I received my B.S. in Systems Engineering and Computer Science from Washington University, working with Humberto Gonzalez on optimal control for hybrid systems.

westenbroekt[at]berkeley[dot]edu  /  CV  /  Scholar

profile photo

Publications

The power of learned locally linear models for nonlinear policy optimization


Dan Pfommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, and Stephen Tu.
ICML 2023 (Under Review), 2023.

Reinforcement Learning with Simple Dynamics Models and Low-Level Feedback Controllers


Tyler Westenbroek, Mohsin Sarwari, Fernando Castaneda, Anand Siththaranjan, Claire Tomlin, Koushil Sreenath, Shankar Sastry.
Under Submission R-AL, 2022.

Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning


Tyler Westenbroek*, Fernando Castaneda, Ayush Agrawal, Shankar Sastry, Koushil Sreenath.
Conference on Robot Learning, 2022.
paper

On the Computational Consequences of Cost Function Design for Nonlinear Optimal Control


Tyler Westenbroek, Anand Siththaranjan, Mohsin Sarwari, Claire Tomlin, Shankar Sastry.
Conference on Decision and Control, 2022.
paper

On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective


Tyler Westenbroek*, Max Simchowitz*, Michael I. Jordan, Shankar Sastry.
Conference On Decision and Control, 2021.
paper

Combining Model-Based Design and Model-Free Policy Optimization to Learn Safe, Stabilizing Controllers


Tyler Westenbroek, Ayush Agrawal, Fernando Castaneda, Shankar Sastry, Koushil Sreenath.
Analysis and Design of Hybrid Systems, 2021.
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Smooth approximations for hybrid optimal control problems with application to robotic walking


Tyler Westenbroek, Xiaobin Xion, Shankar Sastry, Aaron D. Ames.
Analysis and Design of Hybrid Systems, 2021.
paper

Feedback linearization for uncertain systems via reinforcement learning


Tyler Westenbroek*, David Fridovich-Keil*, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, Shankar Sastry, Claire Tomlin.
Internation Conference on Robotics and Automation, 2020.
paper

Learning min-norm stabilizing control laws for systems with unknown dynamics


Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire Tomlin, Shankar Sastry.
Conference on Decision and Control, 2020.
paper

Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning


Fernando Castaneda, Mathias Wulfman, Ayush Agrawal, Tyler Westenbroek, Claire J. Tomlin, S. Shankar Sastry, Koushil Sreenath.
Learning for Dynamics and Control, 2020.
paper

High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems


Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, .
Conference on Decision and Conrtrol, 2020.
paper

Adaptive control for linearizable systems using on-policy reinforcement learning


Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire Tomlin, Shankar Sastry.
Conference on Decision and Control, 2020.
paper

Competitive Statistical Estimation with Strategic Data Sources


Tyler Westenbroek, Roy Dong, Lillian Ratliff, Shankar Sastry.
Transaction on Automatic Control, 2019.
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A New Solution Concept and Family of Relaxations for Hybrid Dynamical Systems


Tyler Westenbroek, Humberto Gonzalez, Shankar Sastry.
Conference on Decision and Control, 2019.
paper

Optimal control of piecewise-smooth control systems via singular perturbations


Tyler Westenbroek, Xiaobin Xiong, Aaron D. Ames, Shankar Sastry.
Conference on Decision and Control, 2019.
paper

Statistical Estimation with Strategic Data Sources in Competitive Settings


Tyler Westenbroek, Roy Dong, Lillian Ratliff, Shankar Sastry.
Conference on Decision and Control, 2017.
paper




Forked from Leonid Keselman.