6 papers
Path Planning in Physically Viable World Models
Su Ann Low, Cheng-Hsi Hsiao, Xingjian Li +3
Robots deployed in unstructured outdoor environments often plan from scene reconstructions collected before deployment because operators cannot remap large or remote sites before e…
Physically Viable World Models: A Case for Query-Conditioned Embodied AI
Adam J. Thorpe, Stepan Tretiakov, Cheng-Hsi Hsiao +6
World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing action outcomes, rather than mer…
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
Cheng-Hsi Hsiao, Krishna Kumar, Ellen M. Rathje
Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn…
From images to properties: a NeRF-driven framework for granular material parameter inversion
Cheng-Hsi Hsiao, Krishna Kumar
We introduce a novel framework that integrates Neural Radiance Fields (NeRF) with Material Point Method (MPM) simulation to infer granular material properties from visual observati…
Investigating the effect of CPT in lateral spreading prediction using Explainable AI
Cheng-Hsi Hsiao, Ellen Rathje, Krishna Kumar
This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an AI model. We em…
Explainable AI models for predicting liquefaction-induced lateral spreading
Cheng-Hsi Hsiao, Krishna Kumar, Ellen Rathje
Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by…