Category: News
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Foundation world models: new preprint online
Our paper develops training-free world models based on foundation models with interpretable latent states. Update: presented at the probabilistic robotics workshop at ICRA’24. Citation:
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TEA Lab moves to Malachowsky Hall
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Now found in Malachowsky 4100, with a brand new racing track coming soon!
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Verifying high-dimensional controllers: new preprint online
Our new draft verifies image-based controllers by approximating them with several low-dimensional ones. Citation: Yuang Geng, Souradeep Dutta, Ivan Ruchkin. Bridging Dimensions: Confident Reachability for High-Dimensional Controllers [arxiv]. Preprint, in submission.
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Ivan participates in a panel on dependable space autonomy
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Resolving Barriers to Infusion: Fielding Dependable Autonomous Space Systems at AIAA ASCEND 2023.
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How safe am I given what I see? New preprint online
Update: a poster was presented at UF AI Days 2023. This paper develops safety chance prediction for image-controlled autonomous systems with calibration guarantees. Citation: Zhenjiang Mao, Carson Sobolewski, Ivan Ruchkin. How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy [arxiv]. Preprint, in submission.
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Invited talk at the DACPS workshop & ETH Autonomy Talks
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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…
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TEA Lab hosts K-12 students for the Robotics-AIoT Visit Day
On June 15, 2023, the UF ECE Department hosted ~30 school students from the Westwood Middle School and Buchholz High School for a day visit at the Robotics, AI, and IoT research laboratories for educational presentations, research demonstrations, and mentoring discussions. It was a lot of fun for everyone! Kudos to the other participating labs:…
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Causal NN controller repair presented at ICAA’23
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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)…
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Conservative safety monitoring presented at NFM’23
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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.