Category: Paper

A new preprint is out on low-dimensional symbolic models of deep visual perception that enable conservative (i.e., non-overconfident) safety analysis.  Citation:  Matthew Cleaveland, Pengyuan Lu, Oleg Sokolsky, Insup Lee, Ivan Ruchkin. Conservative Perception Models for Probabilistic Model Checking [Arxiv]. Preprint, 2025.

Advanced GANs make short work of previously unseen image corruptions. Update: accepted to IROS 2025! Citation:  Carson Sobolewski, Zhenjiang Mao, Kshitij Vejre, Ivan Ruchkin. Generalizable Image Repair for Robust Visual Autonomous Racing [Arxiv] [Poster] [Github] [Video]. Preprint, 2025.

Our new paper articulates four key principles for physical interpretability of world models. We paint a broader picture on neuro-symbolic world models, beyond our recent preprint on a specific technique for physically interpretable world models for trajectory prediction.   Update: accepted and presented at NeuS 2025! It also got publicized at ICRA. Citation:   

Our first collaborative paper on the NSF Neuro-Symbolic Bridge project with RPI is online! It develops a novel way to get tight conformal prediction bounds on perception error in order to improve the accuracy of reachability verification.  Update: published and presented at NeuS’25! Citation:

Check out our nice and short position paper. The key idea is to intermingle neural components and symbolic knowledge at each level of the autonomy stack.   Update: published in FSE’25! Citation:

Our recent preprint develops an architecture and a training method to give latent states physical meaning in the context of trajectory prediction:  

Zhen Lu, Imran Afridi, Hong Jin Kang, Ivan Ruchkin, Xi Zheng. Surveying Neuro-Symbolic Approaches for Reliable Artificial Intelligence of Things [Springer]. In Springer Journal of Reliable Intelligent Environments (JRIE), 2024.  Ankica Barišić, Jácome Cunha, Ivan Ruchkin, Ana Moreira, João Araújo, Moharram Challenger, Dušan Savić, Vasco Amaral. Modelling Sustainability in Cyber-Physical Systems: a Systematic Mapping Study [Elsevier]. In Elsevier Sustainable Computing:…

Our paper gives users of autonomous cars the ability to describe in natural language what conditions they consider nominal or anomalous. Citation: 

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: 

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.