3 papers
cs.RO2026
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…
cs.RO2026
LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation
Fan Yang, Yuting Su, Xiaobo Wang +7
World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve. H…
cs.RO2026
The Gate, Not the Cache: Gate Provenance Bounds the Closed-Loop Reliability of Training-Free VLA Token Skipping
Qi Luo, Shuaijun Liu, Hao Zhao +5
Token skipping is a widely used training-free way to accelerate vision--language--action (VLA) models by bypassing computation for most visual tokens at each control step according…