Controlling for Unsafe Events in Dense Traffic through Autonomous Vehicles: Invited Talk Abstract. - Daniel B. Work; Raphael E. Stern; Fangyu Wu 003; Miles Churchill; Shumo Cui; Hannah Pohlmann; Benjamin Seibold; Benedetto Piccoli; Rahul Bhadani; Matt Bunting; Jonathan Sprinkle; Maria Laura Delle Monache; et al. (SCAV@CPSWeek, 2017)

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments. - Raphael E. Stern; Shumo Cui; Maria Laura Delle Monache; Rahul Bhadani; Matt Bunting; Miles Churchill; Nathaniel Hamilton; R'mani Haulcy; Hannah Pohlmann; Fangyu Wu 003; Benedetto Piccoli; Benjamin Seibold; et al. (CoRR, 2017)

Simulation-Based Reachability Analysis for High-Index Large Linear Differential Algebraic Equations. - Hoang-Dung Tran; Weiming Xiang 001; Nathaniel Hamilton; Taylor T. Johnson (CoRR, 2018)

Verification for Machine Learning, Autonomy, and Neural Networks Survey. - Weiming Xiang 001; Patrick Musau; Ayana A. Wild; Diego Manzanas Lopez; Nathaniel Hamilton; Xiaodong Yang; Joel A. Rosenfeld; Taylor T. Johnson (CoRR, 2018)

Reachability Analysis for High-Index Linear Differential Algebraic Equations. - Hoang-Dung Tran; Luan Viet Nguyen; Nathaniel Hamilton; Weiming Xiang 001; Taylor T. Johnson (FORMATS, 2019)

Case Study: Safety Verification of an Unmanned Underwater Vehicle. - Diego Manzanas Lopez; Patrick Musau; Nathaniel Hamilton; Hoang-Dung Tran; Taylor T. Johnson (SP (Workshops), 2020)

Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability. - Hoang-Dung Tran; Neelanjana Pal; Patrick Musau; Diego Manzanas Lopez; Nathaniel Hamilton; Xiaodong Yang; Stanley Bak; Taylor T. Johnson (CAV (1), 2021)

Reachability Analysis of a General Class of Neural Ordinary Differential Equations. - Diego Manzanas Lopez; Patrick Musau; Nathaniel Hamilton; Taylor T. Johnson (FORMATS, 2022)

Zero-Shot Policy Transfer in Autonomous Racing: Reinforcement Learning vs Imitation Learning. - Nathaniel Hamilton; Patrick Musau; Diego Manzanas Lopez; Taylor T. Johnson (ICAA, 2022)

On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (ICAA, 2022)

Training Agents to Satisfy Timed and Untimed Signal Temporal Logic Specifications with Reinforcement Learning. - Nathaniel Hamilton; Preston Robinette; Taylor T. Johnson (SEFM, 2022)

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (CoRR, 2022)

Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents. - Nathaniel Hamilton; Kyle Dunlap; Taylor T. Johnson; Kerianne L. Hobbs (CoRR, 2022)

Zero-Shot Policy Transfer in Autonomous Racing:Reinforcement Learning vs Imitation Learning. - Nathaniel Hamilton; Patrick Musau; Diego Manzanas Lopez; Taylor T. Johnson (Zenodo, 2022)

On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (Zenodo, 2022)

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles. - Patrick Musau; Nathaniel Hamilton; Diego Manzanas Lopez; Preston Robinette; Taylor T. Johnson (Zenodo, 2022)

Self-Preserving Genetic Algorithms for Safe Learning in Discrete Action Spaces. - Preston K. Robinette; Nathaniel P. Hamilton; Taylor T. Johnson (ICCPS, 2023)

DEMO: Self-Preserving Genetic Algorithms vs. Safe Reinforcement Learning in Discrete Action Spaces. - Preston K. Robinette; Nathaniel P. Hamilton; Taylor T. Johnson (ICCPS, 2023)

Run Time Assurance for Simultaneous Constraint Satisfaction During Spacecraft Attitude Maneuvering. - Cassie-Kay McQuinn; Kyle Dunlap; Nathaniel Hamilton; Jabari Wilson; Kerianne L. Hobbs (CoRR, 2024)

Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles. - Kyle Dunlap; Nathaniel Hamilton; Zachary S. Lippay; Matt Shubert; Sean Phillips; Kerianne L. Hobbs (CoRR, 2024)

Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies. - Kyle Dunlap; Nathaniel Hamilton; Francisco Viramontes; Derrek Landauer; Evan Kain; Kerianne L. Hobbs (CoRR, 2024)

Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls. - Nathaniel Hamilton; Kyle Dunlap; Kerianne L. Hobbs (CoRR, 2024)

Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection. - Kyle Dunlap; Kochise Bennett; David van Wijk; Nathaniel Hamilton; Kerianne Hobbs (CoRR, 2024)

Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection. - Kyle Dunlap; Nathaniel Hamilton; Kerianne L. Hobbs (CoRR, 2024)

The Safe Trusted Autonomy for Responsible Space Program. - Kerianne L. Hobbs; Sean Phillips; Michelle Simon; Joseph B. Lyons; Jared Culbertson; Hamilton Scott Clouse; Nathaniel Hamilton; Kyle Dunlap; Zachary S. Lippay; Joshua Aurand; Zachary I. Bell; Taleri Hammack; et al. (CoRR, 2025)

Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems. - Nathaniel Hamilton; Kyle Dunlap; Kerianne L. Hobbs (CoRR, 2025)