Category: Talk

The papers were on foundation world models and language-enhanced OOD. The audience response was, reportedly, positive and encouraged the implementation on physical robotic systems. Citations: 

In the first presentation of ICCPS 2024, Ivan showcased a method to repair a neural network controller while preserving its verification results.  Citation:  Pengyuan Lu, Matthew Cleaveland, Oleg Sokolsky, Insup Lee, Ivan Ruchkin. Repairing Learning-Enabled Controllers While Preserving What Works [Arxiv] [Github] [Slides]. In Proceedings of the International Conference on Cyber-Physical Systems (ICCPS), Hong Kong, China, 2024. 

Update 1: an extended version of this talk was given at a UF MAE Affiliate Seminar. The recording can be found here (UF login required). Update 2: another version of this walk was given at the ETH Autonomy Talks (video).  Update 3: yet another version of this talks was given as a CNEL Seminar. The…

Shown above is a 5-step workflow of our causal repair: (1) Extract the behaviors of a learning component as an I/O table. (2) Encode the dependency of the desired property outcome on the I/O behaviors with a Halpern-Pearl model. (3) Search for a counterfactual model value assignment, revealing an actual cause and a repair. (4)…

Shown above is our conservative monitoring approach that leverages probabilistic reachability offline and combines it with calibrated state estimation. Citation: Matthew Cleaveland, Oleg Sokolsky, Insup Lee, Ivan Ruchkin. Conservative Safety Monitors of Stochastic Dynamical Systems [ArXiv] [Springer] [Slides]. In Proceedings of the NASA Formal Methods Symposium (NFM), Houston, TX, 2023.