TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Researchers present TANGO, a whole-body vision-language-action model enabling humanoid robots to traverse cluttered spaces from language instructions.
TANGO predicts 29-DoF joint-space actions from egocentric RGB observations and natural-language instructions for whole-body humanoid navigation, going beyond 2D path planning. It is trained entirely in simulation using global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. The model reports state-of-the-art simulation performance and was deployed zero-shot on a Unitree G1 humanoid without any real-world navigation training data.
- First whole-body vision-language navigation framework for language-conditioned humanoid traversal in cluttered environments.
- Predicts 29-DoF joint-space actions from egocentric RGB and natural-language instructions.
- Trained entirely in simulation using path planning, motion generation, and RL-based tracking.
- Deploys zero-shot on a Unitree G1 without any real-world navigation training data.
From the original article
1 min read at arxiv.orgWe 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 · arxiv.org
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