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cs.RO2026
Predict Before You Deploy: Offline Prediction of Quantization-Induced Task Degradation for World Action Models
Jiuyi Xu, Jinjia Guo, Meida Chen +2
World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization reduces memory and can accelerate…
cs.RO2026
PRISM: PRior-guided Imagination Sampling in world Models
Yuhai Wang, Jiawei Xia, Rongxuan Zhou +5
Latent world models learn representations that enable them to predict how actions change the environment - a capability we call physical intuition. We find that these same represen…
cs.RO2023★ 1 cited
Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Jiaohao Wu, Yang Ye, Jing Du
Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind. This paper presents a…