13 papers
Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Haodong Yan, Junfeng Li, Junjie He +12
Mainstream World-Action Models (WAMs) adapt pretrained video generation models (VGMs) for robot control, transferring their learned dynamics prior for action prediction. These VGMs…
Point as Skeleton: Accumulated Point Cloud Enhanced Autoregressive Generation for Closed-Loop Autonomous Driving Simulation
Songbur Wong, Xiaosong Jia, Junqi You +12
Evaluating end-to-end autonomous driving (E2E-AD) remains challenging, as existing driving simulation methods often trade off closed-loop interactivity (e.g., CARLA) and real-world…
InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving
Xiaoyu Ye, Leheng Li, Xinyu Ji +8
Generating realistic, controllable, and temporally coherent urban environments is a critical yet unresolved challenge in the autonomous driving community. In this paper, we introdu…
DualCoT-VLA: Visual-Linguistic Chain of Thought via Parallel Reasoning for Vision-Language-Action Models
Zhide Zhong, Junfeng Li, Junjie He +10
Vision-Language-Action (VLA) models map visual observations and language instructions directly to robotic actions. While effective for simple tasks, standard VLA models often strug…
S-VAM: Shortcut Video-Action Model by Self-Distilling Geometric and Semantic Foresight
Haodong Yan, Zhide Zhong, Jiaguan Zhu +10
Video action models (VAMs) have emerged as a promising paradigm for robot learning, owing to their powerful visual foresight for complex manipulation tasks. However, current VAMs,…
WPT: World-to-Policy Transfer via Online World Model Distillation
Guangfeng Jiang, Yueru Luo, Jun Liu +6
Recent years have witnessed remarkable progress in world models, which primarily aim to capture the spatio-temporal correlations between an agent's actions and the evolving environ…