2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…