TANGO：人形机器人在杂乱环境中的全身导航模型
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
MIT团队发布TANGO模型，让Unitree G1人形机器人能零样本执行语言指令在杂乱环境中导航，无需真实世界训练数据。
TANGO是首个全身视觉语言导航框架，能处理29自由度关节动作。该模型通过模拟训练，可在复杂3D空间中协调手臂放置、躯干调整和步态调节。在模拟实验中，TANGO在视觉语言导航任务上达到最先进性能，并在需要障碍物协商的挑战场景中优于强基线模型。
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
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.
