Publications (14)
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection
Kyle Dunlap, Nathaniel Hamilton, Kerianne L. Hobbs
As the number of spacecraft in orbit continues to increase, it is becoming more challenging for human operators to manage each mission. As a result, autonomous control methods are…
Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles
Kyle Dunlap, Nathaniel Hamilton, Zachary Lippay +3
On-orbit spacecraft inspection is an important capability for enabling servicing and manufacturing missions and extending the life of spacecraft. However, as space operations becom…
The Safe Trusted Autonomy for Responsible Space Program
Kerianne L. Hobbs, Sean Phillips, Michelle Simon +11
The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barrier…
Verification for Machine Learning, Autonomy, and Neural Networks Survey
Weiming Xiang, Patrick Musau, Ayana A. Wild +5
This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereo…
Reachability Analysis of a General Class of Neural Ordinary Differential Equations
Diego Manzanas Lopez, Patrick Musau, Nathaniel Hamilton +1
Continuous deep learning models, referred to as Neural Ordinary Differential Equations (Neural ODEs), have received considerable attention over the last several years. Despite thei…
Simulation-Based Reachability Analysis for High-Index Large Linear Differential Algebraic Equations
Hoang-Dung Tran, Weiming Xiang, Nathaniel Hamilton +1
Reachability analysis is a fundamental problem for safety verification and falsification of Cyber-Physical Systems (CPS) whose dynamics follow physical laws usually represented as…
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents
Nathaniel Hamilton, Kyle Dunlap, Taylor T Johnson +1
Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows. To combat the safety concerns surrou…
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies
Kyle Dunlap, Nathaniel Hamilton, Francisco Viramontes +3
As the number of spacecraft on orbit continues to grow, it is challenging for human operators to constantly monitor and plan for all missions. Autonomous control methods such as re…
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
Nathaniel Hamilton, Kyle Dunlap, Kerianne L. Hobbs
For many space applications, traditional control methods are often used during operation. However, as the number of space assets continues to grow, autonomous operation can enable…
An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles
Patrick Musau, Nathaniel Hamilton, Diego Manzanas Lopez +2
Recent advances in machine learning technologies and sensing have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near…
Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems
Nathaniel Hamilton, Kyle Dunlap, Kerianne L Hobbs
Recent research using Reinforcement Learning (RL) to learn autonomous control for spacecraft operations has shown great success. However, a recent study showed their performance co…
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection
Kyle Dunlap, Kochise Bennett, David van Wijk +2
The trial and error approach of reinforcement learning (RL) results in high performance across many complex tasks, but it can also lead to unsafe behavior. Run time assurance (RTA)…
Run Time Assurance for Simultaneous Constraint Satisfaction During Spacecraft Attitude Maneuvering
Cassie-Kay McQuinn, Kyle Dunlap, Nathaniel Hamilton +2
A fundamental capability for On-orbit Servicing, Assembly, and Manufacturing (OSAM) is inspection of the vehicle to be serviced, or the structure being assembled. This research ass…
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments
Raphael E. Stern, Shumo Cui, Maria Laura Delle Monache +11
Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream,…