arXiv Computer Vision
Sep 16

GeoLAM: Learning Geometry-Grounded Latent Actions from Unlabeled Human Videos

GeoLAM is a framework that learns geometry‑grounded latent actions from unlabeled human videos. It uses future‑frame reconstruction with a frozen geometric feature hierarchy and motion supervision from a 4D geometry teacher to capture 3D displacement, image‑plane motion, and surface‑orientation changes. After pretraining, the representation serves as transition targets for a world‑action model trained on robot demonstrations, enabling denoised latent actions and executable action chunks without requiring hand‑pose annotations or future‑video generation during deployment.

By Yifan Xie, Hekun Tian, Jinkun Liu, YuAn Wang, Qiao Sun, Wenbo Ding
arXiv Computer Vision
Oct 15

OneLA: Learning Many Worlds from Latent Actions

arXiv:2610.20160v1 Announce Type: new Abstract: Learning world models across diverse domains calls for a unified control space, yet ac- tion interfaces vary across domains and action annotations are...

By Ziyu Zhu, Zhaoxu Sun, Fan Jiang, Chiyu Wang, Junfeng Ni, Mu Xu
arXiv AI
Aug 28

CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators

CLAP is a cross-embodiment framework for action‑conditioned video generation that can be trained on diverse internet‑scale videos from both humans and robots. It reconciles different action spaces—end‑effector poses, language instructions, and latent actions—using a curriculum that first learns physics priors from unlabeled video and then grounds them in real‑world action spaces for zero‑shot deployment. The resulting models match or exceed state‑of‑the‑art single‑embodiment models in challenging environments and support few‑shot adaptation across a wide range of robot morphologies.

By Kechen Liu, Ola Shorinwa
arXiv Machine Learning
Jul 8

PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies

arXiv:2602. 19710v3 Announce Type: replace-cross Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-specific action supervision.

By Haitao Lin, Hanyang Yu, Jingshun Huang, He Zhang, Yonggen Ling, Ping Tan, Xiangyang Xue, Yanwei Fu
arXiv AI
Jun 2

From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data

arXiv:2606. 00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models.

By Zhiyuan Feng, Qixiu Li, Huizhi Liang, Rushuai Yang, Yichao Shen, Zhiying Du, Zhaowei Zhang, Yu Deng, Li Zhao, Hao Zhao, Zongqing Lu, Oier Mees, Marc Pollefeys, Jiaolong Yang, Baining Guo
arXiv AI
1d ago

OpenNWM: A Generalist Navigation World Model with Latent Action Pretraining

OpenNWM is a generalist navigation world model that learns universal latent actions from large-scale action‑free videos and aligns them with physical motions through action‑conditioned post‑training. The authors curate NavAnywhere, a dataset of 17.5 million visual observations from 15 heterogeneous sources, covering diverse viewpoints and motion patterns across five scene types. Experiments show that latent‑action pretraining improves visual prediction and navigation performance both in‑domain and out‑of‑domain.

By Dujun Nie, Ruiqi Song, Zixuan Wang, Hanqing Wang, Yifan Han, Baiyong Ding, Wenzhao Zheng, Hangbin Wu, Long Chen