Learning to predict how the world evolves can provide vision-language-action (VLA) policies with predictive context for long-horizon control, but its effectiveness depends on what future representation is modeled and how it conditions action generation. We introduce PLaW-VLA, which models task-relevant future states in...
Yu Liu, He-Tian Guo, Tianlv Huang et al.· 0 citations
Long-term physical coexistence with intelligent robots requires more than capable robot policies. A persistent robotic assistant must support diverse user-facing interfaces, maintain long-horizon memory of people and preferences, coordinate across robot embodiments, and translate human intent into safe physical executi...
Weiqi Jin, Peijun Tang, Kuncheng Luo et al.· 0 citations
Lumo-2 is introduced, a latent world-action model that generates actions by reasoning over world dynamics in latent space that consistently outperforms strong vision-language-action and world-action model baselines, with gains on challenging real-world tasks requiring temporal reasoning, physical understanding, or high...
