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Moo Jin Kim

@moo_jin_kim • 2,162 subscribers

CS PhD Student @Stanford | Prev: Researcher @NVIDIA | AI/ML & Robotics

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We release Cosmos Policy 💫: a state-of-the-art robot policy built on a video diffusion model backbone. - policy + world model + value function — in 1 model - no architectural changes to the base video model - SOTA in LIBERO (98.5%), RoboCasa (67.1%), & ALOHA tasks (93.6%) 🧵👇

We release Cosmos Policy 💫: a state-of-the-art robot policy built on a video diffusion model backbone. - policy + world model + value function — in 1 model - no architectural changes to the base video model - SOTA in LIBERO (98.5%), RoboCasa (67.1%), & ALOHA tasks (93.6%) 🧵👇

148,803 просмотров • 4 месяцев назад

✨ Introducing 𝐎𝐩𝐞𝐧𝐕𝐋𝐀 — an open-source vision-language-action model for robotics! 👐 - SOTA generalist policy - 7B params - outperforms Octo, RT-2-X on zero-shot evals 🦾 - trained on 970k episodes from OpenX dataset 🤖 - fully open: model/code/data all online 🤗 🧵👇

✨ Introducing 𝐎𝐩𝐞𝐧𝐕𝐋𝐀 — an open-source vision-language-action model for robotics! 👐 - SOTA generalist policy - 7B params - outperforms Octo, RT-2-X on zero-shot evals 🦾 - trained on 970k episodes from OpenX dataset 🤖 - fully open: model/code/data all online 🤗 🧵👇

226,922 просмотров • 2 лет назад

Introducing OFT—an Optimized Fine-Tuning recipe for VLAs! Fine-tuning OpenVLA w/ OFT, we see: -25-50x faster inference ⚡️ -SOTA 97.1% avg SR in LIBERO 💪 -high-freq control w/ 7B model on real bimanual robot -outperforms π₀, RDT-1B, DiT Policy, MDT, Diffusion Policy, ACT 🧵👇

Introducing OFT—an Optimized Fine-Tuning recipe for VLAs! Fine-tuning OpenVLA w/ OFT, we see: -25-50x faster inference ⚡️ -SOTA 97.1% avg SR in LIBERO 💪 -high-freq control w/ 7B model on real bimanual robot -outperforms π₀, RDT-1B, DiT Policy, MDT, Diffusion Policy, ACT 🧵👇

84,133 просмотров • 1 год назад

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