TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

2026-09-08

AI Summary

研究解决了人形机器人在杂乱室内环境中的导航问题，通过TANGO框架，结合视觉-语言-动作模型直接预测全身关节动作，实现无碰撞移动。

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.

Problem

• Humanoid Navigation
• Cluttered Environments
• Whole-Body Adaptation
• Collision-Free Movement

Innovation

• Whole-Body Vision-Language Navigation
• Language-Conditioned Humanoid Traversal
• Simulation-to-Real Transfer
• Collision-Free Movement
• Zero-Shot Deployment