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Introducing Subtask Annotations in LeRobot Datasets. LeRobot now supports subtasks as a native dataset field, so you can label task progression frame-by-frame, train hierarchical policies, and build stronger reward models with stage-aware supervision. Available today, with an annotation space + tooling to easily add subtasks and high-level dialogue to...

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LeRobot

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16,478 görüntüleme • 4 ay önce •via X (Twitter)

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Introducing Annotations. Starting today, you can add field notes to your research in Lore with customization options like bold, italics, position, size, and color.
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Introducing Annotations. Starting today, you can add field notes to your research in Lore with customization options like bold, italics, position, size, and color.

zehra ✨

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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: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:
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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: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

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🚀 Introducing the Earth Engine Dataset Explorer, powered by Google #Gemini! 🌍✨ Struggling to find the right #geospatial datasets for your research? Our Dataset Explorer recommends tailored datasets and offers an interactive exploration experience. 📹 Check out the video tutorial: 📒 Explore the notebook: #geemap #OpenSource #AI #EarthEngine
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🚀 Introducing the Earth Engine Dataset Explorer, powered by Google #Gemini! 🌍✨ Struggling to find the right #geospatial datasets for your research? Our Dataset Explorer recommends tailored datasets and offers an interactive exploration experience. 📹 Check out the video tutorial: 📒 Explore the notebook: #geemap #OpenSource #AI #EarthEngine

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Introducing Bambot, an open source, low cost (~$300) humanoid robot. Inspired by LeRobot , so-100 arm and lekiwi
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Introducing Bambot, an open source, low cost (~$300) humanoid robot. Inspired by LeRobot , so-100 arm and lekiwi

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🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here:
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🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here:

Unitree

5,629,954 görüntüleme • 2 ay önce

(1/2) Introducing annotations in Mixboard! With the annotations tool, you can doodle directly on your board to add or edit elements. Just mark the area, describe it, and watch it appear.
0:24

(1/2) Introducing annotations in Mixboard! With the annotations tool, you can doodle directly on your board to add or edit elements. Just mark the area, describe it, and watch it appear.

Google Labs

60,516 görüntüleme • 7 ay önce

We're introducing EnvHub to LeRobot! Upload simulation environments to the @hugginigface hub, and load them in lerobot, with one line of code ! env = lerobot.make_env("username/my-env", trust_remote_code=True) Back in 2017, OpenAI called on the community to build Gym environments. Today, we're doing the same for robotics. We're calling researchers, students, hobbyists, labs, and startups to publish complex envs, tasks, and benchmarks on the Hub, so we see how robot policies perform. (in Isaaac, Mujoco, Genesis, etc..) Fill out and submit the form linked in the comments to join the effort!
0:36

We're introducing EnvHub to LeRobot! Upload simulation environments to the @hugginigface hub, and load them in lerobot, with one line of code ! env = lerobot.make_env("username/my-env", trust_remote_code=True) Back in 2017, OpenAI called on the community to build Gym environments. Today, we're doing the same for robotics. We're calling researchers, students, hobbyists, labs, and startups to publish complex envs, tasks, and benchmarks on the Hub, so we see how robot policies perform. (in Isaaac, Mujoco, Genesis, etc..) Fill out and submit the form linked in the comments to join the effort!

jade

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Ropedia Xperience-10M is out on Hugging Face a large-scale egocentric multimodal dataset of human experience for embodied AI, robotics, world models, and spatial intelligence It contains 10 million experiences (interaction) and 10,000 hours of synchronized first-person recordings with six video streams, audio, stereo depth, camera pose, hand mocap, full-body mocap, IMU, and hierarchical language annotations dataset:
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Ropedia Xperience-10M is out on Hugging Face a large-scale egocentric multimodal dataset of human experience for embodied AI, robotics, world models, and spatial intelligence It contains 10 million experiences (interaction) and 10,000 hours of synchronized first-person recordings with six video streams, audio, stereo depth, camera pose, hand mocap, full-body mocap, IMU, and hierarchical language annotations dataset:

AK

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We're developing a native LeRobot data loader. It's a work in progress because the datasets vary greatly, but it's starting to be useful. Try it out with the latest 0.22.1 release! If you encounter any snags, let us know so we can resolve them quickly. cc Remi Cadene
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We're developing a native LeRobot data loader. It's a work in progress because the datasets vary greatly, but it's starting to be useful. Try it out with the latest 0.22.1 release! If you encounter any snags, let us know so we can resolve them quickly. cc Remi Cadene

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Introducing AI fashion Photoshoots on Flair AI you can now train fashion models on your clothing with incredible accuracy - texture, labels, logos, and more preserved with midjourney level quality 🔥 rt and comment to try this feature in beta. serving access codes today:)
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Introducing AI fashion Photoshoots on Flair AI you can now train fashion models on your clothing with incredible accuracy - texture, labels, logos, and more preserved with midjourney level quality 🔥 rt and comment to try this feature in beta. serving access codes today:)

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Introducing Composer Actions - Developers can now build apps that modify new casts - Create a bounty, a poll or an event and automatically generate a frame - Developers build ongoing relationships with users in an app they already have and use everyday
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Introducing Composer Actions - Developers can now build apps that modify new casts - Create a bounty, a poll or an event and automatically generate a frame - Developers build ongoing relationships with users in an app they already have and use everyday

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Introducing Unsloth Studio ✨ A new open-source web UI to train and run LLMs. • Run models locally on Mac, Windows, Linux • Train 500+ models 2x faster with 70% less VRAM • Supports GGUF, vision, audio, embedding models • Auto-create datasets from PDF, CSV, DOCX • Self-healing tool calling and code execution • Compare models side by side + export to GGUF GitHub: Blog and Guide: Available now on Hugging Face, NVIDIA, Docker and Colab.

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Did you know that in Blender you can easily create a turntable animation with a single driver expression?✨ 1. Make an empty 2. Parent your camera to it 3. In the empty's Z rotation field write "# frame / 300 * pi * 2", where 300 is your final frame 4. Enjoy the loop! #b3d

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Welcome to the CORSAIR FRAME 5000D! Assemble with ease and showcase your build in our new stunning mid-tower PC case. Available in EU now ⤵
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Welcome to the CORSAIR FRAME 5000D! Assemble with ease and showcase your build in our new stunning mid-tower PC case. Available in EU now ⤵

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Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

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Sydnie has a new addition to her kitchen with the Skylight Frame. Now her family and friends can add their favorite photos to her frame no matter where they are.

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Introducing the EASIEST way to fine-tune Qwen 2 VL with a Hugging Face dataset! (Link to repo in first reply). You can either fine-tune inside or a Gradio app or with a single line of Python. Simply select your favorite image-text dataset and fine-tune! A special thanks to Faen Zhang and all those at HuggingFace and those behind Qwen 2 VL. blog coming soon!

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