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🚀 First step to unlocking Generalist Robots! Introducing 🤖LAPA🤖, a new SOTA open-sourced 7B VLA pretrained without using action labels. 💪SOTA VLA trained with Open X (outperforming OpenVLA on cross and multi embodiment) 😯LAPA enables learning from human videos, unlocking potential for robotic foundation model ❗Over 30x pretraining efficiency... show more

Seonghyeon Ye

1,761 subscribers

33,289 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

LAPA consists of a 1) Latent Action Quantization and 2) Latent Pretraining stage. The first stage learns quantized actions through visual deltas. For the second stage, a pretrained VLM (LWM) is trained to predict the quantized latent actions. During finetuning, we map the latent actions to real actions.

Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

LAPA beats OpenVLA across cross- and multi-embodiment tasks, all without using action labels during pretraining! 🚀 A step forward in robust, generalizable robot learning.

Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

We can built LAPA from 220K human videos where the action labels does not exist and the embodiment gap is huge. Still, LAPA (Human Videos) outperform OpenVLA (Bridge) 😯

Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

What do latent actions mean? Latent actions correspond to 🌟semantic 🌟actions across different robot embodiments. Interestingly, despite different robot embodiments, the same latent action maps to similar movements. This suggests latent actions form a ‘shared’ representation space, much like images or language.

Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

We also do closed-loop rollout of LAPA for analysis (rollout vs ground truth). Given ‘pick up the broccoli from pot,’ it successfully picks up the object, which then disappears. This highlights LAPA’s potential as an emerging 'world model' with impressive predictive abilities! 🌍

Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

All of the things are open-sourced! Code 💻: Huggingface 🤗: Website🌐:

Profilbild von Seonghyeon Ye

Seonghyeon Yevor 1 Jahr

Co-led with @jang_yoel With wonderful collaborators: Byeongguk Jeon, @joocjun ,@jw2yang4ai Baolin Peng, @AjayMandlekar, Reuben Tan, Yu-Wei Chao, @billyuchenlin, Lars Liden And advisors: @kimin_le2, @JianfengGao0217, @LukeZettlemoyer, Dieter Fox, @seo_minjoon from @kaist_ai, @UW, @Microsoft, @nvidia, @allen_ai

Profilbild von Lingxuan Wu

Lingxuan Wuvor 1 Jahr

Firm step towards scaling up VLA model！Excellent job！

Profilbild von Jiazhi Yang

Jiazhi Yangvor 1 Jahr

Incredible job! I view it as the first practical evidence that unsupervised latent action could work well at such a large scale.

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NVIDIA Robotics

13,456 Aufrufe • vor 6 Monaten

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

60,912 Aufrufe • vor 6 Monaten

💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.

💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.

Ilir Aliu - eu/acc

17,353 Aufrufe • vor 11 Monaten