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Spatial AI ( is building large real-world datasets to teach robots how to navigate the world and complete tasks. Their first open source dataset, SEA (Spatial Everyday Activities), is the largest curated egocentric dataset of people carrying out real tasks, with 10,000 hours of data.

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Y Combinator

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20,944 просмотров • 8 месяцев назад •via X (Twitter)

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Over the Reality just hit another major milestone in the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.
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Over the Reality just hit another major milestone in the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.

Over the Reality 🌐

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A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.
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A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.

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Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.
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Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.

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Analyzing large vector datasets on Source Cooperative with #DuckDB 🦆Using the 75 GB National Wetlands Inventory as an example. It used to take me hours to get summary statistics of the dataset, now it takes seconds with DuckDB 🚀 Dataset: Colab: #geospatial #dataviz #opendata
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Analyzing large vector datasets on Source Cooperative with #DuckDB 🦆Using the 75 GB National Wetlands Inventory as an example. It used to take me hours to get summary statistics of the dataset, now it takes seconds with DuckDB 🚀 Dataset: Colab: #geospatial #dataviz #opendata

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Today, we're launching the world's largest open-source dataset of computer-use recordings. 10,000+ hours across Salesforce, Blender, Photoshop and more, to automate the next level of white-collar work. Link in the comments :) Markov
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The largest open-source humanoid teleop dataset, 10+ TB collected from real homes, is now available to everyone. Proud to support BitRobot 🦾's journey in pushing real robotics to the frontier at Virtuals Protocol.
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The largest open-source humanoid teleop dataset, 10+ TB collected from real homes, is now available to everyone. Proud to support BitRobot 🦾's journey in pushing real robotics to the frontier at Virtuals Protocol.

Virtuals Protocol

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How can robots reliably place objects in diverse real-world tasks? 🤖🔍 Placement is tough—objects vary in shape and placement modes (such as stacking, hanging, and insertion), making it a challenging problem. We introduce AnyPlace, a two-stage method trained purely on synthetic data to predict diverse placement poses of unseen objects for real-world tasks. Read on for more👇
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How can robots reliably place objects in diverse real-world tasks? 🤖🔍 Placement is tough—objects vary in shape and placement modes (such as stacking, hanging, and insertion), making it a challenging problem. We introduce AnyPlace, a two-stage method trained purely on synthetic data to predict diverse placement poses of unseen objects for real-world tasks. Read on for more👇

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25,378 просмотров • 1 год назад

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned
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We are proud to announce our first contribution to open source Computer Vision research! 🚀 Today, we’re publishing on Hugging Face a 1,000 3D Maps Dataset under CC-BY-NC license, designed to accelerate progress in 3D perception and spatial AI. Our way to give back to the global AI community and show how DePIN can make an impact. 🧵 👉
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We are proud to announce our first contribution to open source Computer Vision research! 🚀 Today, we’re publishing on Hugging Face a 1,000 3D Maps Dataset under CC-BY-NC license, designed to accelerate progress in 3D perception and spatial AI. Our way to give back to the global AI community and show how DePIN can make an impact. 🧵 👉

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We’re building the first real AI + on-chain product suite on Keeta Network and early users own a piece of it. We’re giving 5% of $KTAI to the community via our Founders Airdrop. Complete tasks. Refer friends. Climb the leaderboard. → Let the race begin.

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Big congrats to the whole team at Giza for the release their new product Giza World. I was given the opportunity to turn their dataset of more than 16.000 active agents, performing more than 800.000 transactions, into a living data sculpture. Kind of mind-blowing to experience the end result, knowing that each movement of each individual particle is backed by real, on-chain, transactions.

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Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ

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How can we address the scarcity of data required for specialized AI? Learn about Simula, a framework that reframes synthetic data generation as dataset-level mechanism design. By using reasoning to architect datasets from first principles, Simula enables fine-grained control over coverage, complexity, and quality. More →

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The world is coming together. 48 teams. 12 groups. Millions of devices. As the world prepares for football's biggest stage, another global network is already in motion. Powered by real people, real devices, and AI-native infrastructure, the UpRock Swarm spans every continent - connecting communities, powering intelligence, and building the future of the AI Web. This is just the beginning. Road to the World Cup Powered by UpRock

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