4 papers
MoWM: Mixture-of-World-Models for Embodied Planning via Latent-to-Pixel Feature Modulation
Yangcheng Yu, Xin Jin, Yu Shang +4
Embodied action planning is a core challenge in robotics, requiring models to generate precise actions from visual observations and language instructions. While video generation wo…
UrbanWorld2.0: A Multimodal Agentic Framework for Reality-Aligned 3D World Generation at City-Scale
Shengyuan Wang, Zhiheng Zheng, Yu Shang +6
The automated generation of high-fidelity, city-scale 3D environments remains a formidable challenge with profound academic and industrial implications. However, existing methods s…
Mem4Nav: Boosting Vision-and-Language Navigation in Urban Environments with a Hierarchical Spatial-Cognition Long-Short Memory System
Lixuan He, Haoyu Dong, Zhenxing Chen +3
Vision-and-Language Navigation (VLN) in large-scale urban environments requires embodied agents to ground linguistic instructions in complex scenes and recall relevant experiences…
Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models
Haohan Chi, Huan-ang Gao, Ziming Liu +12
Vision-Language-Action (VLA) models for autonomous driving show promise but falter in unstructured corner case scenarios, largely due to a scarcity of targeted benchmarks. To addre…