6 papers
World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models
Jiuyi Xu, Xiao Hu, Meida Chen +3
Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures.…
VLAQuantBench: Closed-Loop Evaluation of Post-Training Quantization for Vision-Language-Action Models
Jiuyi Xu, Qing Jin, Meida Chen +3
Post-training quantization reduces the memory requirements of vision-language-action (VLA) models, but precision selection must account for the interaction between layer scope, num…
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…
LowDiff: Efficient Diffusion Sampling with Low-Resolution Condition
Jiuyi Xu, Qing Jin, Meida Chen +3
Diffusion models have achieved remarkable success in image generation but their practical application is often hindered by the slow sampling speed. Prior efforts of improving effic…
IDU: Incremental Dynamic Update of Existing 3D Virtual Environments with New Imagery Data
Meida Chen, Luis Leal, Yue Hu +5
For simulation and training purposes, military organizations have made substantial investments in developing high-resolution 3D virtual environments through extensive imaging and 3…
Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework
Jiuyi Xu, Meida Chen, Andrew Feng +2
In the domain of the U.S. Army modeling and simulation, the availability of high quality annotated 3D data is pivotal to creating virtual environments for training and simulations.…