From 6b8e312ce1826da7e7889b910836312a8a2cc511 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Mon, 7 Jul 2025 14:36:54 -0400 Subject: [PATCH] vault backup: 2025-07-07 14:36:54 --- ... the Safety Assurance of Machine Learning Controllers.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md b/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md index 43ed998b4..5f8d22122 100644 --- a/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md +++ b/Notes on Papers/On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers.md @@ -13,16 +13,20 @@ to talking about the formal verification of the switching mechanism, but they do make note that's future work. **Contributions:** -They show how a simplex system can work and the difficulties +They show how a simplex system can work and the difficulties of the *sim2real* transition for machine learning controllers. **Clarity:** +Really nicely written. # Second Pass **What is the main thrust?** +They use a simplex style controller setup with real time reachability to know when to use a optimal ML based controller vs. a safety oriented controller. They use the reachability to do this in real time, and demonstrate how different ML models line up against one another. **What is the supporting evidence?** +They ran a whole bunch of experiments. They published all of the results, with their main metrics being Mean ML usage. **What are the key findings?** +The biggest findings are that when obstacles are introduced (or other general advesary behavior), # Third Pass **Recreation Notes:**