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Unitree majorly fully open-sources the UnifoLM-WLA-1.0 embodied foundation model, achieving new SOTA results across multiple benchmarks among open-source models worldwide.

Unitree

150,177 subscribers

7,346,498 次观看 • 12 天前 •via X (Twitter)

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6 条评论

Teoria Da Constatação 🔎 的头像

Teoria Da Constatação 🔎12 天前

👀

olligo 的头像

olligo12 天前

@adcock_brett a new challenger wants to enter the arena

Albert Delavega 的头像

Albert Delavega12 天前

Palhaçada !

Clémence Bellerose 的头像

Clémence Bellerose11 天前

Enfin une vraie ouverture dans l'embodied AI, bravo à toute l'équipe Unitree !

Λέανδρος 🇧🇷 的头像

Λέανδρος 🇧🇷11 天前

Good suave!

HALAN SILVA MENEZES 的头像

HALAN SILVA MENEZES11 天前

!🤖+INNOVATION+🤖!💥😉😎💥😉😎💥

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Unitree G1 Open Source Dataset In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated: Open source data collection: Open source learning algorithms: Open source datasets and models: #AI #Teleoperation #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI

Unitree G1 Open Source Dataset In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated: Open source data collection: Open source learning algorithms: Open source datasets and models: #AI #Teleoperation #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI

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Introducing Gaze-LLE, a new model for gaze target estimation built on top of a frozen visual foundation model! Gaze-LLE achieves SOTA results on multiple benchmarks while learning minimal parameters, and shows strong generalization paper:

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Fiona Ryan

427,067 次观看 • 1 年前

We're releasing HY-Embodied-0.5, a family of foundation models for real-world embodied agents. The 2B model is now open source. It strengthens spatial-temporal perception and embodied reasoning for prediction, interaction, and planning. 🤖 The suite includes: 🔹 2B for edge deployment 🔹 32B for complex reasoning Key innovations: 🔹 Mixture-of-Transformers (MoT) architecture for modality-specific computation 🔹 Latent tokens for improved perceptual representation 🔹 Self-evolving post-training 🔹 On-policy distillation from large to small models Across 22 benchmarks, the 2B model outperforms similarly sized SOTA systems on 16 tasks. The 32B model approaches frontier-level performance. 🔗 GitHub: 🤗 Hugging Face:

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