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pith:3NSFLYB5

pith:2024:3NSFLYB5XM5L4DIY4H65GBVNE4
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Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks

Haoran Liu, He Wang, Jiazhao Zhang, Kunyu Wang, Minghan Li, Shaoan Wang, Songlin Wei, Zhizheng Zhang, Zhongyuan Wang

A single video-based model unifies multiple robot navigation tasks by standardizing their data formats.

arxiv:2412.06224 v2 · 2024-12-09 · cs.RO · cs.CV

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Claims

C1strongest claim

Uni-NaVid is the first video-based vision-language-action model designed to unify diverse embodied navigation tasks and enable seamless navigation for mixed long-horizon tasks in unseen real-world environments.

C2weakest assumption

Harmonizing input and output data configurations across tasks allows effective integration and positive synergy in learning without loss of performance on individual tasks or introduction of negative interference.

C3one line summary

Uni-NaVid unifies diverse embodied navigation tasks into one video-based vision-language-action model trained on 3.6 million samples from four sub-tasks, achieving state-of-the-art performance on benchmarks and real-world tests.

References

126 extracted · 126 resolved · 13 Pith anchors

[1] Etpnav: Evolving topological planning for vision-language nav- igation in continuous environments 2023
[3] On Evaluation of Embodied Navigation Agents 2018 · arXiv:1807.06757
[4] Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments 2018
[5] Sim-to-real transfer for vision-and-language navigation 2021
[6] Human memory: A proposed system and its control processes (vol 1968

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Cited by

27 papers in Pith

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First computed 2026-05-17T23:38:46.784204Z
Builder pith-number-builder-2026-05-17-v1
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db6455e03dbb3abe0d18e1fdd306ad272fa57104b1f13a1816e9da3eaae1b047

Aliases

arxiv: 2412.06224 · arxiv_version: 2412.06224v2 · doi: 10.48550/arxiv.2412.06224 · pith_short_12: 3NSFLYB5XM5L · pith_short_16: 3NSFLYB5XM5L4DIY · pith_short_8: 3NSFLYB5
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/3NSFLYB5XM5L4DIY4H65GBVNE4 \
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# expect: db6455e03dbb3abe0d18e1fdd306ad272fa57104b1f13a1816e9da3eaae1b047
Canonical record JSON
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