The paper introduces LaPla, a Vision‑Language‑Action framework that uses a latent‑aligned planning approach to convert discrete semantic reasoning into continuous, physics‑constrained driving actions. It employs a residual VQ‑VAE to encode vehicle kinematics into a structured latent space, then projects multimodal inputs—images, past actions, and text—directly into this latent space, allowing a frozen decoder to generate physically plausible trajectories without quantization errors. Experiments on nuScenes and NVIDIA AlpaSim show LaPla reduces long‑horizon L2 error by 15.52% and improves closed‑loop success rates by 33.34 percentage points while cutting inference latency.
By Ruoyu Yao, Yusen Xie, Qingzhao Liu, Pei Liu, Zewei Yang, Yipeng Zhu, Xiaolong Wang, Jun Ma
arXiv:2606. 02753v1 Announce Type: cross Abstract: Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a single agent observing from a single perspective.
By Teng Hu, Mingchun Lu, Yating Wang, Jiangning Zhang, Jinkun Hao, Ye Pan, Ran Yi, Lizhuang Ma, Dacheng Tao
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
By Mingkai Jia, Jiaxin Guo, Zhijian Shu, Jiawei Xu, Mingxiao Li, Jintao Cheng, Ping Tan, Wei Yin
arXiv:2603. 02697v2 Announce Type: replace-cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction.
By Jiayi Zhu, Jianing Zhang, Yiying Yang, Wei Cheng, Xiaoyun Yuan
arXiv:2608. 07267v1 Announce Type: new Abstract: Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observations and language instructions directly to navigation actions.
By Yuehao Huang, Yunzi Wu, Xiaotao Zhang, Xinhai Li, Jiankun Dong, Jiajun Lv, Chi Zhang, Chenjia Bai, Yong Liu, Xuelong Li
RotVLA introduces a Vision‑Language‑Action framework that replaces discrete latent action encoding with a continuous rotational latent action representation on the group SO(n). This design provides continuity, compositionality, and structured geometry that better capture real‑world action dynamics, and a triplet frame learning scheme enforces meaningful temporal dynamics while preventing degeneration. Trained with 1.7 B parameters on large cross‑embodiment datasets, RotVLA achieves state‑of‑the‑art performance on LIBERO and RoboTwin2.0 benchmarks and shows strong real‑world manipulation results.
By Qiwei Li, Xicheng Gong, Xinghang Li, Peiyan Li, Quanyun Zhou, Hangjun Ye, Jiahuan Zhou, Yadong Mu
arXiv:2607.10744v5 Announce Type: replace
Abstract: Benefiting from the powerful priors embedded in large-scale pre-training data and the emerging commonsense reasoning ability, large language models...
By Changfei Fu, Guangcheng Chen, Aoxiang Gu, Haoxiang Liang, Wenjun Xu, Hong Zhang
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.
By Chen Yang, Yuhao Wei, Ze Xu, Ziheng Zou, Shuang Liang, Delin Ouyang, Lingfeng Qi, Jie Li, Guofa Li
arXiv:2606. 24472v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-language backbones, but their visual tokens remain grounded in 2D image coordinates rather than the calibrated geometry of the robot's cameras -- a mismatch especially pronounced in multi-camera setups, where views are coupled by known intrinsics and extrinsics yet processed as independent images.
By Yue Peng, Yongzhe Zhao, Artur Habuda, Khuyen Pham, Yanheng Zhu, Tran Nguyen Le, Fares Abu-Dakka, Li Guo
arXiv:2605.10426v3 Announce Type: replace-cross
Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mecha...
By Minqing Huang, Yujiao Xiang, Zihan Liang, Jiajie Huang, Jingqi Wang, Yuheng Zhou, Zhi Xu, Feiyang Tan, Hangning Zhou, Mu Yang, Gong Che
arXiv:2609.39195v1 Announce Type: new
Abstract: Spatial intelligence requires maintaining a coherent understanding of the world as the embodied agent moves. Like humans, the agent must use its own mo...
By Shichao Li, Meiqi Wang, Fei Su, Zhicheng Zhao
ZYT-World is a real‑time, controllable world model designed for closed‑loop autonomous‑driving simulation. It generates a full fisheye‑pinhole camera rig at native resolution, using Plucker adapters for camera geometry, ego‑motion adaptive layer normalization for motion control, and a lightweight pixel‑aligned layout to condition traffic participants. The model achieves high fidelity to a 40‑step teacher while being over 100 times faster, and includes an implicit‑memory module that preserves place‑specific evidence for long‑horizon stability.
By Boni Hu, Xiong Wei, Haoming Huang, Yong Huang, Chenbo Wang, Yi Yang, Jiancheng Wang, Ruicheng Zhu, Zhimin Yang, Guanglai Liu, Qiaowan Jin, Dongzhuo Wang, Haiwei Kuang, Jiajun Fan, Yue Wu, Jiaxin Wei, Hao Sun, Feihong Yan, Wei Bi, Kaixuan Wang, Zichao Guo, Xiaozhi Chen