WholebodyVLA README

The project's README from github.com/OpenDriveLab/WholebodyVLA. As one markdown file: README.md.

WholeBodyVLA Logo

Towards Unified Latent VLA for Whole-body Loco-manipulation Control

Paper Home

✒️ Haoran Jiang*, Jin Chen*, Qingwen Bu, Li Chen, Modi Shi, Yanjie Zhang, Delong Li, Chuanzhe Suo, Chuang Wang, Zhihui Peng†, Hongyang Li†

📧 Primary Contact: Haoran Jiang ([email protected]).

🔥 Highlights

📋 Overview

WholeBodyVLA is a unified Vision-Language-Action framework for large-space humanoid loco-manipulation. It learns unified latent actions from action-free egocentric videos through a Latent Action Model (LAM), and employs a loco-manipulation-oriented (LMO) RL policy for precise and stable whole-body coordination. The system encodes egocentric images and language instructions into latent action tokens, which are decoded into dual-arm joint actions and locomotion commands, enabling end-to-end control for complex loco-manipulation tasks.

WholeBodyVLA Method

See more on project website.

📝 Note: We currently have no concrete timeline for open-sourcing the codebase. This repository now serves as a collection of resources and references for the whole-body humanoid VLA research community. We welcome discussion and collaboration!

Let's go for VLA on humanoids!

Awesome Vision–Language–Action for Humanoid Robots

A curated list of research on Vision–Language–Action (VLA) models and related for humanoid robots, with a focus on loco-manipulation task.

Continuously updating...

Perception & Planning for Humanoids

Vision-Based Perception for Planning

Manipulation

Other Tasks

MoCap-Based Planning

Generative Motion and Trajectory Planning

Whole-Body Controller for Loco-Manipulation

Behavior Foundation Models / Universal Whole-Body Tracking

Upper-Body Centric

Hardware Design

Data Collection Systems

Capability Extension

Generalist Vision–Language–Action Models

Manipulation

Learning from Human Videos

Navigation

References

We refer to and recommend several curated paper lists and repositories:

Contributors

This project is contributed by: Jin Chen, Yucheng Huang, Haoran Jiang, Yixuan Pan, Shijia Peng, Jialong Zeng, Hai Zhang.

All names are listed in alphabetical order by last name.

Citation

If you find this repository helpful, please consider citing:

@article{jiang2025wholebodyvla,
  title={WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control}, 
  author={Jiang, Haoran and Chen, Jin and Bu, Qingwen and Chen, Li and Shi, Modi and Zhang, Yanjie and Li, Delong and Suo, Chuanzhe and Wang, Chuang and Peng, Zhihui and Li, Hongyang},
  journal={arXiv preprint arXiv:2512.11047},
  year={2025}
}

@article{chen2025intelligent,
  title={Intelligent Robot Manipulation Requires Self-Directed Learning},
  author={Chen, Li and Sima, Chonghao and Chitta, Kashyap and Loquercio, Antonio and Luo, Ping and Ma, Yi and Li, Hongyang}, 
  year={2025}
}

WholebodyVLA (OpenDriveLab/WholebodyVLA)

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580 ·GitHub
WholebodyVLA
这是什么？WholeBodyVLA 是一个 ICLR 2026 研究项目，提出了用于人形机器人全身移动-操作闭环控制的统一视觉-语言-动作（Vision-Language-Action）框架，这个仓库同时也是一个精选、持续更新的人形机器人 VLA 论文列表。
这是一个能运行的代码库，还是只有论文和阅读清单？它主要是论文概览加一个「Awesome Vision-Language-Action for Humanoid Robots」阅读清单；WholeBodyVLA 的代码本身尚未发布。
这个仓库实际怎么用？不需要安装任何东西——你浏览分类的论文列表（感知/规划、全身控制器、生成式运动规划、基于 MoCap 的规划），然后顺着每个条目的 arXiv、Code 或 Demo 链接查看。
我现在能自己运行 WholeBodyVLA 模型吗？不能——作者没有给出代码库的发布时间表，目前只有论文的描述和链接的参考文献列表可用。
WholeBodyVLA 在技术上是怎么工作的？它通过潜在动作模型（Latent Action Model，LAM）从无动作标签的第一人称视频中学习统一的潜在动作，把图像和语言指令编码为潜在动作 token，再解码为双臂关节动作和移动指令，并由面向移动-操作的（LMO）强化学习策略引导，实现稳定的全身协调。
这个 awesome 列表是怎么组织的？论文按主题分组，比如面向操作的视觉感知、基于 MoCap 的规划、生成式运动/轨迹规划和全身控制器，每个条目都标注了发表场合、年份，以及（如有）arXiv、代码或演示链接。
这个列表还在更新吗？在更新——它明确标注为「Continuously updating」（持续更新），并且已经收录了最新到 2026 年初的条目。
What are similar projects?awesome-rsi, Motus2, RoboICL, Zero-WAM, lerobot, transferlearning, Embodied-AI-Start, Embodied-AI-Guide
Which topics is it in?vision-language-action, robot, embodied-ai, papers, paper, robotics, academic, reinforcement-learning, multimodal, awesome, artificial-intelligence, computer-vision
Creator
OpenDriveLab
Size
44.9 MB
Stars
580
Updated
CommitMay 25
Age
10mo
Commits · 90 Days