TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Researchers introduce TANGO, a whole-body vision-language-action model enabling zero-shot language-guided humanoid navigation on the Unitree G1 robot.
TANGO addresses humanoid navigation in cluttered indoor environments by predicting 29-DoF joint-space actions directly from natural-language instructions and egocentric RGB, rather than 2D path planning. It is trained entirely in simulation via a pipeline combining global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. In simulation it achieves state-of-the-art vision-language navigation performance and transfers zero-shot to a Unitree G1 humanoid without any real-world navigation data.
- First whole-body vision-language navigation framework for language-conditioned humanoid traversal in clutter.
- Predicts 29-DoF joint-space actions from instructions and egocentric RGB directly.
- Trained entirely in simulation using path planning, motion editing, and RL-based tracking.
- Deploys zero-shot on Unitree G1 without any real-world navigation data.
- Vendors
- Unitree
- Products
- Unitree G1TANGO
- AI models
- TANGO
From the original article
1 min read at huggingface.co
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.
By Anqi Li, Yuxin Chen, Zhaobo Li · huggingface.co
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