| title | TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model |
|---|---|
| authors | Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah |
| date | 2026-09-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.09158 |
| upvotes | 7 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| primer | Vision-language-action models |
| abstract | We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requires continuous geometry-aware whole-body adaptation, including coordinated arm placement, torso adjustment, and gait modulation for collision-free movement through complex 3D spaces. We introduce TANGO, the first whole-body vision-language navigation framework for language-conditioned humanoid traversal in cluttered environments. Given a natural-language instruction and egocentric RGB observations, TANGO directly predicts 29-DoF joint-space actions for downstream whole-body control. We train TANGO entirely in simulation by synthesizing diverse collision-free traversal behaviors via global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. This pipeline provides dynamically feasible action supervision for learning language-conditioned whole-body policies. In extensive simulation experiments, TANGO demonstrates state-of-the-art performance in vision-language navigation, while outperforming strong modular baselines in navigating challenging scenes requiring obstacle negotiation. Lastly, we deploy TANGO zero-shot on a Unitree G1 humanoid robot, and observe robust language-guided traversal in cluttered real-world scenes without training on any real-world navigation data. |
cite
@misc{li2026tango,
title={TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model},
author={Anqi Li and Yuxin Chen and Zhaobo Li and Zhuo Cao and Junli Ren and Masayoshi Tomizuka and Dhruv Shah},
year={2026},
eprint={2609.09158},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.09158}
}