3 citations · 3 across the 10 of their papers we have counts for
13 papers
Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Itô Frameworks
Thomas Lew
Stochastic differential equations (SDEs) can be studied via Itô calculus and rough path theory. For stochastic optimal control, these two frameworks give distinct Pontryagin Maximu…
Emergent Autonomous Drifting for Collision Avoidance in Real-World Winter Driving Scenarios
Elliot Weiss, Michael Thompson, Thomas Lew +1
Real-world collision avoidance is a core motivation for studying the dynamics and control of high sideslip drifting in vehicles, yet the practical benefit of such maneuvers has so…
TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU
Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov +2
Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference. For model predictive control (MPC) to scale with this paradigm, so…
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
Ray Zhang, Marcus Greiff, Thomas Lew +1
We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. T…
Vision-Conditioned Variational Bayesian Last Layer Dynamics Models
Paul Brunzema, Thomas Lew, Ray Zhang +3
Agile control of robotic systems often requires anticipating how the environment affects system behavior. For example, a driver must perceive the road ahead to anticipate available…
Dynamic Association of Semantics and Parameter Estimates by Filtering
Marcus Greiff, Ray Zhang, Thomas Lew +1
We propose a probabilistic semantic filtering framework in which parameters of a dynamical system are inferred and associated with a closed set of semantic classes in a map. We ext…
Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size
Thomas Lew, Marcus Greiff, John Subosits +1
Proximal methods such as the Alternating Direction Method of Multipliers (ADMM) are effective at solving constrained quadratic programs (QPs). To tackle infeasible QPs, slack varia…
Differentiable Model Predictive Control on the GPU
Emre Adabag, Marcus Greiff, John Subosits +1
Differentiable model predictive control (MPC) offers a powerful framework for combining learning and control. However, its adoption has been limited by the inherently sequential na…
MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries
François Grolleau, Emily Alsentzer, Timothy Keyes +17
Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assur…
Control Barrier Functions for Shared Control and Vehicle Safety
James Dallas, John Talbot, Makoto Suminaka +4
This manuscript presents a control barrier function based approach to shared control for preventing a vehicle from entering the part of the state space where it is unrecoverable. T…
Rough Stochastic Pontryagin Maximum Principle and an Indirect Shooting Method
Thomas Lew
We derive first-order Pontryagin optimality conditions for stochastic optimal control with deterministic controls for systems modeled by rough differential equations (RDE) driven b…
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
Alexander Davydov, Franck Djeumou, Marcus Greiff +4
Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, on…
Risk-Averse Model Predictive Control for Racing in Adverse Conditions
Thomas Lew, Marcus Greiff, Franck Djeumou +3
Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle con…