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

• URL: http://arxiv.org/abs/2609.09158v1
• Date: Tue, 08 Sep 2026 17:59:55 GMT
• Title: TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
• Abstract summary: We study the problem of navigating cluttered indoor environments with a humanoid robot.<n>We introduce TANGO, the first whole-body vision-language navigation framework.<n>In simulation experiments, TANGO demonstrates state-of-the-art performance in vision-language navigation.
• Score: 49.03909082056646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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