3 papers
cs.LG2026
Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world models reduce the reliance on c…
cs.RO2026
ST-VLA: Enabling 4D-Aware Spatiotemporal Understanding for General Robot Manipulation
You Wu, Zixuan Chen, Cunxu Ou +9
Robotic manipulation in open-world environments requires reasoning across semantics, geometry, and long-horizon action dynamics. Existing hierarchical Vision-Language-Action (VLA)…
cs.RO2026
Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Data-efficient learning remains a central challenge in autonomous driving due to the high cost and safety risks of large-scale real-world interaction. Although world-model-based re…