4 papers · 1 filter
What Should Robot Policies Learn to Predict? Rethinking Supervision Targets in Vision-Language-Action Learning
Haitao Shen, Hanyu Liu, Yihan Lin +7
Vision-language-action (VLA) policies are typically trained with action prediction as the primary supervision signal, since robots ultimately execute actions. Auxiliary objectives…
What Limits Cross-Task Generalization in World Action Models?
Chen Zhao, Zipeng Ye, Yihan Lin +2
Cross-task generalization remains difficult for world action models (WAMs), even when their predicted futures depict plausible manipulation. This gap can be attributed directly: we…
JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling
Yihan Lin, Jiawei He, Shifeng Bao +6
Robust robot control benefits from explicitly modeling state transitions, but video-generation world action models (WAMs) introduce substantial deployment cost. Existing latent WAM…
Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception
Taoyi Wang, Lijian Wang, Yihan Lin +4
Perception systems in robotics encounter significant challenges in high-speed and dynamic conditions when relying on traditional cameras, where motion blur can compromise spatial f…