From the 1 of 6 linked papers with an AI index.
6 papers
ABot-3DWorld 0: A Universal World Model to Explore Any 3D Space
Mingchao Sun, Luyang Tang, Yu Liu +34
The paper introduces ABot-3DWorld 0, a multimodal system that converts text, images, or video into high‑fidelity, explorable 3D worlds using a compact spatial representation and pa…
DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
Ziying Song, Lin Liu, Hongyu Pan +7
Most end-to-end autonomous driving methods rely on imitation learning from single expert demonstrations, often leading to conservative and homogeneous behaviors that limit generali…
ABot-Earth 0.5: Generative 3D Earth Model
Ming Qian, Tianjian Ouyang, Mingchao Sun +25
We present ABot-Earth 0.5, a generative 3D framework designed to synthesize vast, seamless 3D environments from ubiquitous, geospatially referenced satellite imagery. To achieve th…
See It, Say It, Sorted: An Iterative Training-Free Framework for Visually-Grounded Multimodal Reasoning in LVLMs
Yongchang Zhang, Oliver Ma, Tianyi Liu +2
Recent large vision-language models (LVLMs) have demonstrated impressive reasoning ability by generating long chain-of-thought (CoT) responses. However, CoT reasoning in multimodal…
Fully Unified Motion Planning for End-to-End Autonomous Driving
Lin Liu, Caiyan Jia, Ziying Song +6
Current end-to-end autonomous driving methods typically learn only from expert planning data collected from a single ego vehicle, severely limiting the diversity of learnable drivi…
Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +7
End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control…