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TANGO framework enables humanoid robots to navigate cluttered spaces using language

By PulseAugur Editorial · [2 sources] ·

Researchers have developed TANGO, a novel whole-body vision-language navigation framework for humanoid robots. This system enables robots to navigate cluttered indoor environments based on natural language instructions, predicting 29-DoF joint-space actions for precise whole-body control. TANGO was trained entirely in simulation and demonstrated state-of-the-art performance, even outperforming modular baselines in complex obstacle negotiation scenarios. Notably, the framework was successfully deployed zero-shot on a Unitree G1 humanoid robot, achieving robust navigation in real-world cluttered spaces without any real-world training data. AI

IMPACT This framework could significantly advance humanoid robot autonomy in complex, real-world environments by enabling natural language-guided navigation.

RANK_REASON The cluster describes a new research paper detailing a novel framework for humanoid robot navigation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

TANGO framework enables humanoid robots to navigate cluttered spaces using language

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The cluster describes a new research paper detailing a novel framework for humanoid robot navigation.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah ·

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

    arXiv:2609.09158v1 Announce Type: cross 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 continu…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    TANGO is a vision-language framework that predicts whole-body joint actions for humanoid robots to navigate cluttered indoor environments using only simulated training data.

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