WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control

WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control

|Authors: Haoran Jiang, Jin Chen, Qingwen Bu, Li Chen, Modi Shi, Yanjie Zhang, Delong Li, Chuanzhe Suo, Chuang Wang, Zhihui Peng, Hongyang Li

|Venue: arXiv preprint | Year: 2025

|arXiv: 2512.11047v2

Abstract

Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion. This confines the robot to a limited workspace, preventing it from performing large-space loco-manipulation. We attribute this to: (1) the challenge of acquiring loco-manipulation knowledge due to the scarcity of humanoid teleoperation data, and (2) the difficulty of faithfully and reliably executing locomotion commands, stemming from the limited precision and stability of existing RL controllers. To acquire richer loco-manipulation knowledge, we propose a unified latent learning framework that enables Vision-Language-Action (VLA) system to learn from low-cost action-free egocentric videos. Moreover, an efficient human data collection pipeline is devised to augment the dataset and scale the benefits. To execute the desired locomotion commands more precisely, we present a loco-manipulation-oriented (LMO) RL policy specifically tailored for accurate and stable core loco-manipulation movements, such as advancing, turning, and squatting. Building on these components, we introduce WholeBodyVLA, a unified framework for humanoid loco-manipulation. To the best of our knowledge, WholeBodyVLA is one of its kind enabling large-space humanoid loco-manipulation. It is verified via comprehensive experiments on the AgiBot X2 humanoid, outperforming prior baseline by 21.3%. It also demonstrates strong generalization and high extensibility across a broad range of tasks.

Key Contributions

• Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco…
• Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion
• This confines the robot to a limited workspace, preventing it from performing large-space loco-ma…
• We attribute this to: (1) the challenge of acquiring loco-manipulation knowledge due to the scarc…
• To acquire richer loco-manipulation knowledge, we propose a unified latent learning framework tha…

Topics

• manipulation
• locomotion
• vision
• vla
• reinforcement-learning
• control
• learning-from-demonstration
• benchmark

Code & Data

• GitHub repository: https://github.com/OpenDriveLab/WholebodyVLA

BibTeX

@article{Jiang2025_251211047v2,
  title     = {WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control},
  author    = {Haoran Jiang and Jin Chen and Qingwen Bu and Li Chen and Modi Shi and Yanjie Zhang and Delong Li and Chuanzhe Suo and Chuang Wang and Zhihui Peng and Hongyang Li},
  year      = {2025},
  eprint    = {2512.11047v2},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2512.11047v2}
}