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Temporal and spatial alignment between glove and camera data. It is not an easy task, but it’s coming together. Human data for robots. #Robotics

Yu Xiang

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50,151 views • 6 months ago •via X (Twitter)

Science & Technology #Robotics

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Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵

Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵

Ilir Aliu

37,370 views • 5 months ago

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Pierre-Alexandre Balland

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Holding $tsla is not easy but it’s worth it and that makes it easy.

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AI runs on data. but most of it is stale, outdated, static. what if intelligence was powered by living signals from real human activity? introducing: 𝐳𝐃𝐚𝐭𝐚 - the Data Layer Powering Zentry and The AI Data Economy ⇢

AI runs on data. but most of it is stale, outdated, static. what if intelligence was powered by living signals from real human activity? introducing: 𝐳𝐃𝐚𝐭𝐚 - the Data Layer Powering Zentry and The AI Data Economy ⇢

Zentry

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Each of us navigates our surroundings using sight and sound together. The sensor system in the Rivian Autonomy Platform works the same way. It takes data from cameras, radar and – coming soon – LiDAR, then fuses them together in an instant. Not as separate inputs but as one complete picture of the world that’s more precise than any single type of data could be on its own.

Each of us navigates our surroundings using sight and sound together. The sensor system in the Rivian Autonomy Platform works the same way. It takes data from cameras, radar and – coming soon – LiDAR, then fuses them together in an instant. Not as separate inputs but as one complete picture of the world that’s more precise than any single type of data could be on its own.

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81,840 views • 7 months ago

💡Data Scraping vs. Data Mining: Understanding the Difference Ever wondered why OptimAI Network emphasizes Data Mining over simple web scraping? Here’s why: 🔹Web Scraping is surface-level, capturing raw data from websites without context, validation, or depth. 🔹Data Mining, however, is a deeper, intelligent process—extracting, analyzing, validating, and refining data into structured insights essential for advanced AI models. 💫 Why OptimAI Focuses on Data Mining? OptimAI's decentralized nodes don’t just collect data—they actively validate, annotate, and refine it. By leveraging collective human intelligence, edge computing, and autonomous AI agents, we deliver the high-quality, real-world data necessary for powering sophisticated AI. 💫 Why It Matters—and Why You Should Join Now Participate now via our OptimAI Lite Node, and become a foundational part of building the most comprehensive decentralized Reinforcement Data Network for Agentic AI. Mine data, fuel innovation, and earn OPI rewards. 👉 Chrome Extension Node: 👉 Telegram Node: Together, let's redefine data for #DePIN #AI.

💡Data Scraping vs. Data Mining: Understanding the Difference Ever wondered why OptimAI Network emphasizes Data Mining over simple web scraping? Here’s why: 🔹Web Scraping is surface-level, capturing raw data from websites without context, validation, or depth. 🔹Data Mining, however, is a deeper, intelligent process—extracting, analyzing, validating, and refining data into structured insights essential for advanced AI models. 💫 Why OptimAI Focuses on Data Mining? OptimAI's decentralized nodes don’t just collect data—they actively validate, annotate, and refine it. By leveraging collective human intelligence, edge computing, and autonomous AI agents, we deliver the high-quality, real-world data necessary for powering sophisticated AI. 💫 Why It Matters—and Why You Should Join Now Participate now via our OptimAI Lite Node, and become a foundational part of building the most comprehensive decentralized Reinforcement Data Network for Agentic AI. Mine data, fuel innovation, and earn OPI rewards. 👉 Chrome Extension Node: 👉 Telegram Node: Together, let's redefine data for #DePIN #AI.

OptimAI Network

51,095 views • 1 year ago

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

20,944 views • 8 months ago

China is scaling humanoid robotics at an insane speed. Super realistic robots from Ex-Robots are now reportedly entering mass production 🤖 - Not prototypes. - Not lab experiments. - Actual production. The biggest shift happening in robotics isn’t just intelligence anymore. It’s making robots look and behave socially acceptable around humans. And honestly… we’re reaching the point where some people may not immediately realize they’re talking to a robot. Exciting future or uncomfortable future? Media : Ex-Robots ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

China is scaling humanoid robotics at an insane speed. Super realistic robots from Ex-Robots are now reportedly entering mass production 🤖 - Not prototypes. - Not lab experiments. - Actual production. The biggest shift happening in robotics isn’t just intelligence anymore. It’s making robots look and behave socially acceptable around humans. And honestly… we’re reaching the point where some people may not immediately realize they’re talking to a robot. Exciting future or uncomfortable future? Media : Ex-Robots ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

CTO ROBOTICS Media

13,284 views • 2 months ago

📢 Nuklai Insights: Building Multi Datasets for Next Generation Insights Full Blog: We live in an era where #data is valuable, but only a few organizations mine ⛏️ insights from data. Idle data is now a cost, a risk, and an opportunity lost. $NAI

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Axis Robotics

27,858 views • 6 months ago

🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.

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TheNewPhysics

20,485 views • 1 month ago

Some robots run fast. Some run like humans! And honestly, the second kind is more interesting—like this: HONOR Yuanqi. From the perspective of embodied intelligence, "running like a human" is not just an aesthetic choice—it’s a functional milestone. That's where robotics starts to shift from performance… to understanding.

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Everyone is selling robotics data. Most of it isn't what you actually need. The right kind of data depends entirely on what you're training. And that's the question almost no one asks before the check gets cut.

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🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

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“A choice between hate and success. A choice between vengeance and peace. But for me it’s not much of a choice is it? For me they are one and the same.” “I have to choose between hate and success. Between peace and vengeance. But then I was reminded, they’re one and the same”

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Open-Sourced Robotics Datasets Have Exploded This Year, Turning The Field Into A More Scalable And Collaborative Ecosystem. We can expect major breakthroughs in the very near future; the data for robotics is exploding!

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Crypto decisions are often driven by noise and emotion. But the future belongs to clarity, data, and transparency. CrypGPT is built with an AI-first mindset — because crypto should be smarter, not louder. AI • Data • Blockchain #CrypGPT #CryptoAI #Web3Community #CryptoInnovation #DataDriven #CryptoCommunity #DigitalAssets

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Gemini-powered robot can now effectively debug itself! I've been obsessed with two main questions in robotics: can robots learn from their own mistakes without humans in the loop, and how much can we leverage synthetic data? Spoiler: yes, and it's surprisingly elegant once you have the right primitives in place. The architecture is fairly simple (and optimized for GPU_Poor users): Component I: Gemini Brain ♊️ - Gemini 2.0 Flash analyzes all training episodes through both camera perspectives - Gemini 2.0 Pro creates a summary of training data, highlighting biases, limitations, etc. - Train policy p0 on this initial data, run evaluation episodes - Ask Gemini to categorize successes vs. failures (more insightful than you'd expect) - Based on both analyses, Gemini generates specific augmentation recommendations What's interesting here isn't that we're using LLMs for robotics - it's that we're closing the loop between perception, failure analysis, and targeted data generation. Component II: Data Generation with Scene Consistency The tricky part was maintaining consistency across both camera perspectives while generating new data. Three current augmentations: - Frame flipping and polarity reversals - Grounded-SAM + OpenCV for object color manipulation - Gemini to identify empty space and generate distractions in the scene …and repeat, ha! I'm using the so100 robot arm and Sarah’s Vintage from Hugging Face. And the APIs and models in Gemini family are Ace! Thank you Logan Kilpatrick Patrick Loeber and team for this. In thread The Circus of Making It Actually Work🧵:

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Cleanliness on camera, but waste allegedly dumped into the Ganga River This is not awareness it’s hypocrisy. The Ganga is not just a river, it’s faith for millions. Real change needs responsibility and action, not just content for the camera.

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