# TANGO：面向杂乱环境的全身视觉-语言-动作人形机器人导航框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-08 08:00
- AIHOT 分数：46
- AIHOT 链接：https://aihot.news/items/cmttmozwp0ejfrofpg2jrcsvg
- 原文链接：https://arxiv.org/abs/2609.09158

## AI 摘要

TANGO 提出首个面向杂乱环境的全身视觉-语言导航框架，支持语言指令下的人形机器人穿越复杂 3D 空间。该模型直接预测 29-DoF 关节空间动作，完全在仿真中训练，通过全局路径规划、运动编辑与 RL 跟踪合成无碰撞行为。TANGO 在仿真中达到 SOTA 性能，并零样本部署至 Unitree G1 人形机器人，无需真实导航数据即可实现稳健的语言引导穿越。

## 正文

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.