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Human Data. Robot Data. Virtual Data. BrainCo Dexterous Data Matrix connects diverse data sources to establish a closed-loop data ecosystem, enabling robots to learn from real-world experiences and achieve more natural interactions. #BrainCo #EmbodiedAI #FutureRobotics

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Tulsi Soni

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67,586 görüntüleme • 4 gün önce •via X (Twitter)

Bilim & Teknoloji#BrainCo#EmbodiedAI#FutureRobotics
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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)
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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

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🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) #CVPR2026, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗
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🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) #CVPR2026, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗

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Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵
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Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

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Mecka is excited to power EgoVerse: a growing ecosystem for robot learning from egocentric human data Proven across 4 leading research labs, EgoVerse data consistently boosts robot performance We release the tools for anyone to collect data, inference/train, and contribute! 🧵
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Mecka is excited to power EgoVerse: a growing ecosystem for robot learning from egocentric human data Proven across 4 leading research labs, EgoVerse data consistently boosts robot performance We release the tools for anyone to collect data, inference/train, and contribute! 🧵

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Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild 🙌🏕️ Human data collection system that works in diverse environments, without robots 💪🦾 Human + Robot Cotraining pipeline that unlocks generalization 🧵👇
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Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild 🙌🏕️ Human data collection system that works in diverse environments, without robots 💪🦾 Human + Robot Cotraining pipeline that unlocks generalization 🧵👇

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Robot-free demos can be collected through our VR interface, inspected, trained, and evaluated in a closed loop. Cool thing is that a small amount of real-robot data mixed with large-scale robot-free data can reach comparable performance, while reducing real-robot data needs by up to 20x. Code: Paper:

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Bernt Børnich says teleoperation data and synthetic data are like a crutch to bootstrap your way to robots learning in the real world.
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Bernt Børnich says teleoperation data and synthetic data are like a crutch to bootstrap your way to robots learning in the real world.

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For roboticists, one challenge towers above the others: there isn’t enough data. To accelerate the deployment of intelligent robots in the real world, MIT CSAIL’s "LucidSim" uses genAI & physics engines to create diverse & realistic virtual training grounds for robots. W/o any real-world data, their robot achieved expert-level performance in difficult tasks:
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For roboticists, one challenge towers above the others: there isn’t enough data. To accelerate the deployment of intelligent robots in the real world, MIT CSAIL’s "LucidSim" uses genAI & physics engines to create diverse & realistic virtual training grounds for robots. W/o any real-world data, their robot achieved expert-level performance in difficult tasks:

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Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)
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Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

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Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

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1/ Cherry AI Robotics is building the world’s first data layer for robotics. Every autonomous robot needs a neural network to operate, which requires massive domain-specific data. Cherry AI Robotics has an entire ecosystem of products with 200k+ communities and 800k+ users. These products generate large amounts of data, which is parsed to create valuable data sets for robotics. Learn more:

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Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

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Train the AI behind real-world robots We’re hiring Data Labelers to annotate images and videos to train Optimus & autonomous systems. You’ll work with real production data & directly impact how these systems learn and operate in the real world Come join!

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This year is truly the dawn of the “robot data oil” rush--spawning all kinds of pick-and-shovel tools to dig it up. X Square Robot, which offers in-home humanoid cleaning services, has just launched a full UMI/Egocentric data collection system: QUANXTA Zero. Real robots delivering actual on-site services is one powerful data collection method. Meanwhile, bodiless (no physical robot) UMI/Ego-centric( human-centric)data pipelines are also surging in popularity. Well,everyone’s racing to distill the physical world and extract the data that truly matters for advancing robot intelligence.
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This year is truly the dawn of the “robot data oil” rush--spawning all kinds of pick-and-shovel tools to dig it up. X Square Robot, which offers in-home humanoid cleaning services, has just launched a full UMI/Egocentric data collection system: QUANXTA Zero. Real robots delivering actual on-site services is one powerful data collection method. Meanwhile, bodiless (no physical robot) UMI/Ego-centric( human-centric)data pipelines are also surging in popularity. Well,everyone’s racing to distill the physical world and extract the data that truly matters for advancing robot intelligence.

CyberRobo

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1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n
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1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

Zhi (Leo) Wang

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1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.
1:10

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

_victorad

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