12 citations · 13 across the 14 of their papers we have counts for
14 papers
Uncertainty Estimators for Robust Backup Control Barrier Functions
David E. J. van Wijk, Ersin Das, Anil Alan +5
Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the…
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…
Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics
David E. J. van Wijk, Samuel Coogan, Tamas G. Molnar +2
Obtaining a controlled invariant set is crucial for safety-critical control with control barrier functions (CBFs) but is non-trivial for complex nonlinear systems and constraints.…
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)…