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cs.RO2026
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.…
cs.RO2026
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…
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…