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Boyi Li

@Boyiliee

Efficient Multimodality for Embodied Intelligence
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Boyi Li @Boyiliee
Embodied intelligence will be unlocked by efficient multimodal learning, but multimodal learning is far bigger than just VLMs or VLAs.
Jitendra MALIK Verified account @JitendraMalikCV Jun 5
I want to offer some unsolicited advice to computer vision researchers jumping into robotics. Don't focus too much on VLMs, VLAs etc. That's fine, but the real action is at the sensorimotor level. Most of the open problems in robotics are in manipulation, which is about hand-object interaction, and contacts and forces are central. Proprioception and tactile sensing are as important as vision. Don't get seduced by cherry-picked demos. You can't do robotics without doing robotics.
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Boyi Li @Boyiliee
Autogaze for video understanding! Up to 4-100x token savings, 19x speedup, and enables scaling to 4K-res 1K-frame videos.
Baifeng Verified account @baifeng_shi Mar 24
Humans can see in high-res, high-FPS in real-time. Why can't VLMs?

Introducing AutoGaze: ViTs/VLMs "gaze" only at key video regions! Up to 4-100x token savings, 19x speedup, and enables scaling to 4K-res 1K-frame videos.

📄 arxiv.org/abs/2603.12254
🌐 autogaze.github.io
🤗 huggingface.co/collections/bf…

(1/n)🧵
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Boyi Li @Boyiliee
Why does motion matter in robotics, autonomy, and beyond?

When a robot grasps a cup or a self-driving car navigates traffic, it’s not just computing, it’s learning through motion.

Read more 👇
sites.google.com/site/boyilics/…
sites.google.com Boyi Li - FoundationMotion Why motion matters in robotics, autonomy, and beyond Every time a robot reaches for a cup or an autonomous vehicle steers through traffic, it isn’t just computing, it’s feeling through motion....
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Boyi Li @Boyiliee
Proud to be part of the team 🚗 - Alpamayo
Marco Pavone Verified account @drmapavone Jan 5
🚀 Exciting news from #CES2026!

In his keynote today, Jensen announced @nvidia Alpamayo — a *fully open* ecosystem of models, simulation tools, and datasets designed to accelerate reasoning-based autonomous vehicle (AV) architectures and advance the path to Level 4 autonomous driving.

Alpamayo brings together several technologies we’ve developed to enable reasoning-based vision–language–action (VLA) models for AVs. Our goal is to provide researchers and developers with a flexible, fast, and scalable platform for evaluating and training reasoning-based AV architectures in realistic closed-loop settings.

Explore Alpamayo:
-- Press Release: nvidianews.nvidia.com/news/alpamayo-…

-- Hugging Face Blog: huggingface.co/blog/drmapavon…

-- Tech Blog: developer.nvidia.com/blog/building-…

-- Alpamayo 1 reasoning model: research.nvidia.com/publication/20…

-- Physical AI AV Dataset: huggingface.co/datasets/nvidi…

-- AlpaSim simulator: github.com/NVlabs/alpasim

I’m incredibly proud of the @nvidia AV Research team (research.nvidia.com/labs/avg/) and our many @nvidia collaborators whose contributions made this possible.

More releases and features are coming soon — we can’t wait to see what the community builds with Alpamayo!

💡 Want to help grow the Alpamayo ecosystem? We’re hiring:
[Sr.] Research Scientist: nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx…
[Sr.] Research Engineer: nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx…

#AutonomousVehicles #AutonomousDriving #AI #Simulation #ReasoningAI #OpenEcosystem #Alpamayo @NVIDIAAI @NVIDIADRIVE
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Boyi Li @Boyiliee
Introducing FoundationMotion.
A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding.
Fully open-source: code, datasets, and models, free to use and build on.

Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets.

We present FoundationMotion, a fully automated pipeline that:
• detects & tracks objects in videos
• extracts trajectories
• uses LLMs + frames to generate rich motion captions & QA pairs
→ creating large-scale, high-quality motion datasets at scale.

After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios.

📜Paper: arxiv.org/abs/2512.10927
🌐Webpage: yulugan.com/projects/Found…
💻 Code: github.com/Wolfv0/Foundat…
🕸️Model: huggingface.co/WoWolf/models
📊 Dataset: huggingface.co/datasets/WoWol…
👉 Interactive Demo: huggingface.co/spaces/yulu2/F…

Let’s move research forward together.

FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series: wolfv0.github.io.
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