2 citations · 4 across the 3 of their papers we have counts for
3 papers
Characterising the Robustness of Reinforcement Learning for Continuous Control using Disturbance Injection
Catherine R. Glossop, Jacopo Panerati, Amrit Krishnan +2
In this study, we leverage the deliberate and systematic fault-injection capabilities of an open-source benchmark suite to perform a series of experiments on state-of-the-art deep…
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
Zhaocong Yuan, Adam W. Hall, Siqi Zhou +4
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained…
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke, Melissa Greeff, Adam W. Hall +4
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learni…