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🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing...

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Lukas Ziegler

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40,583 次观看 • 2 个月前 •via X (Twitter)

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AGIBOT introduces AgiBot World, a large-scale open-source dataset for general-purpose robotic learning. ⦿ 1M+ trajectories from 100+ real-world scenarios ⦿ Scenarios include dexterous manipulation, tool use, and multi-robot collaboration
1:16

AGIBOT introduces AgiBot World, a large-scale open-source dataset for general-purpose robotic learning. ⦿ 1M+ trajectories from 100+ real-world scenarios ⦿ Scenarios include dexterous manipulation, tool use, and multi-robot collaboration

The Humanoid Hub

59,847 次观看 • 1 年前

Earlier this year, AgiBot open-sourced a large-scale dataset of real-world humanoid robot tasks. Now, they've announced AgiBot Digital World, a large-scale simulation framework that includes an open-sourced dataset covering 180+ object categories and 12 core skills.
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Earlier this year, AgiBot open-sourced a large-scale dataset of real-world humanoid robot tasks. Now, they've announced AgiBot Digital World, a large-scale simulation framework that includes an open-sourced dataset covering 180+ object categories and 12 core skills.

The Humanoid Hub

43,709 次观看 • 1 年前

🚀 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:
1:27

🚀 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

5,630,541 次观看 • 3 个月前

Cortex AI (CortexAI) produces the world's most diverse egocentric and robot dataset, captured in real workplace environments. Leading research labs use Cortex AI to collect data and deploy robotics foundation models in real-world settings.
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Cortex AI (CortexAI) produces the world's most diverse egocentric and robot dataset, captured in real workplace environments. Leading research labs use Cortex AI to collect data and deploy robotics foundation models in real-world settings.

Y Combinator

16,336 次观看 • 7 个月前

Unitree G1 Open Source Dataset In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated: Open source data collection: Open source learning algorithms: Open source datasets and models: #AI #Teleoperation #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI
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Unitree G1 Open Source Dataset In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated: Open source data collection: Open source learning algorithms: Open source datasets and models: #AI #Teleoperation #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI

Unitree

148,860 次观看 • 1 年前

Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.
1:41

Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.

RoboHub🤖

43,066 次观看 • 9 个月前

🤖⚙️AGIBOT G2: The Next Generation of Industrial-Grade Interactive Embodied Operation Robots Sub-millimeter precision, human-like motion, 24/7 non-stop operation — AGIBOT G2 is the industrial-grade interactive embodied operation robot your factory needs! Dual arms with ≤0.5N force control for submillimeter accuracy, 26-DOF full-body movement, and NVIDIA-powered ultra computing. From high-precision assembly to logistics sorting, it’s redefining human-robot collaboration across every industry. The future of industrial automation is here. #AGIBOT #HumanoidNo1 #HumanoidShipmentsNo1 #AGIBOTG2 #IndustrialRobotics #Automation #SmartManufacturing
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🤖⚙️AGIBOT G2: The Next Generation of Industrial-Grade Interactive Embodied Operation Robots Sub-millimeter precision, human-like motion, 24/7 non-stop operation — AGIBOT G2 is the industrial-grade interactive embodied operation robot your factory needs! Dual arms with ≤0.5N force control for submillimeter accuracy, 26-DOF full-body movement, and NVIDIA-powered ultra computing. From high-precision assembly to logistics sorting, it’s redefining human-robot collaboration across every industry. The future of industrial automation is here. #AGIBOT #HumanoidNo1 #HumanoidShipmentsNo1 #AGIBOTG2 #IndustrialRobotics #Automation #SmartManufacturing

AGIBOT

21,290 次观看 • 4 个月前

China-based TARS Robotics demonstrated a humanoid robot performing two-handed hand embroidery during a public showcase on December 22, marking a notable step in fine motor control for humanoid systems. The robot threaded a needle and stitched a logo using both hands with sub-millimeter accuracy, working with soft, flexible materials that are difficult for traditional industrial robots to handle due to deformation and variability. According to the company, this capability is enabled by a closed-loop "Data-Al-Physics" system that connects real-world data collection, embodied Al models, and physical execution, reducing the gap between simulation and real deployment. The models are said to be open source when released. Founded in February 2025, TARS Robotics has moved quickly from research to live demonstrations and has raised over $240 million in early funding from the Chinese government, reflecting growing interest in general-purpose humanoid robots for precision and dexterous tasks.
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China-based TARS Robotics demonstrated a humanoid robot performing two-handed hand embroidery during a public showcase on December 22, marking a notable step in fine motor control for humanoid systems. The robot threaded a needle and stitched a logo using both hands with sub-millimeter accuracy, working with soft, flexible materials that are difficult for traditional industrial robots to handle due to deformation and variability. According to the company, this capability is enabled by a closed-loop "Data-Al-Physics" system that connects real-world data collection, embodied Al models, and physical execution, reducing the gap between simulation and real deployment. The models are said to be open source when released. Founded in February 2025, TARS Robotics has moved quickly from research to live demonstrations and has raised over $240 million in early funding from the Chinese government, reflecting growing interest in general-purpose humanoid robots for precision and dexterous tasks.

Brian Roemmele

82,611 次观看 • 6 个月前

Humanoid Robots on a Real Production Line: AGIBOT Livestream From June 23–28, AGIBOT will host a livestream from a tablet mass-production line, showcasing humanoid robots operating across a full quality-inspection section in a real factory environment alongside human operators. Over six days, viewers will get a live look at real production workflows, human-robot collaboration, multi-robot coordination, and long-duration operations under actual industrial conditions. As humanoid robotics moves from demonstrations to real-world deployment, the livestream offers a rare opportunity to see how humanoid robots perform on an active production line. 📅 June 23–28, 2026 📍 Longcheer Technology, Nanchang Factory ▶ Watch live on AGIBOT Official X（ Youtube ( Channel.
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Humanoid Robots on a Real Production Line: AGIBOT Livestream From June 23–28, AGIBOT will host a livestream from a tablet mass-production line, showcasing humanoid robots operating across a full quality-inspection section in a real factory environment alongside human operators. Over six days, viewers will get a live look at real production workflows, human-robot collaboration, multi-robot coordination, and long-duration operations under actual industrial conditions. As humanoid robotics moves from demonstrations to real-world deployment, the livestream offers a rare opportunity to see how humanoid robots perform on an active production line. 📅 June 23–28, 2026 📍 Longcheer Technology, Nanchang Factory ▶ Watch live on AGIBOT Official X（ Youtube ( Channel.

AGIBOT

81,560 次观看 • 18 天前

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:
0:46

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Jim Fan

332,199 次观看 • 2 年前

Headless Open-Source Humanoid Robot Starts at $7,999 San Francisco’s Almond Robotics just launched the Axol--a capable dual-arm platform built for real-world Physical AI. >860mm reach, 6.5kg payload, 500Hz control >Fully US-assembled, open-source Python SDK + VR teleop >Modular, Interchangeable end-effectors,developer-friendly design Data collection, performing tasks in a real-world environment: assembly, packaging, sorting... --- Following the U.S. government's ban on Chinese humanoid robots, domestic U.S. humanoid robot startups have begun to emerge.
1:18

Headless Open-Source Humanoid Robot Starts at $7,999 San Francisco’s Almond Robotics just launched the Axol--a capable dual-arm platform built for real-world Physical AI. >860mm reach, 6.5kg payload, 500Hz control >Fully US-assembled, open-source Python SDK + VR teleop >Modular, Interchangeable end-effectors,developer-friendly design Data collection, performing tasks in a real-world environment: assembly, packaging, sorting... --- Following the U.S. government's ban on Chinese humanoid robots, domestic U.S. humanoid robot startups have begun to emerge.

CyberRobo

12,611 次观看 • 25 天前

This giant free dataset could make helper robots way smarter, way faster: An open-source robotics stack from Berkeley AI researchers featuring the largest teleoperation dataset released to date with over 3,500 hours of bimanual manipulation data across 200 tasks. The video showcases autonomous bimanual robot performance on dexterous tasks including box folding, Lego sorting, AirPod insertion, t-shirt folding, backpack packing, and box unlocking using learned policies. Sim-to-real correlations, training insights like flow loss predicting real-world success, lightweight infrastructure for DAgger interventions Thank you for sharing, Ritvik Singh, and everyone else who contributed to this! Links to the paper plus dataset at under permissive licensing. ——- Weekly robotics and AI insights. Subscribe free:
0:38

This giant free dataset could make helper robots way smarter, way faster: An open-source robotics stack from Berkeley AI researchers featuring the largest teleoperation dataset released to date with over 3,500 hours of bimanual manipulation data across 200 tasks. The video showcases autonomous bimanual robot performance on dexterous tasks including box folding, Lego sorting, AirPod insertion, t-shirt folding, backpack packing, and box unlocking using learned policies. Sim-to-real correlations, training insights like flow loss predicting real-world success, lightweight infrastructure for DAgger interventions Thank you for sharing, Ritvik Singh, and everyone else who contributed to this! Links to the paper plus dataset at under permissive licensing. ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

13,264 次观看 • 17 天前

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
1:23

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

46,829 次观看 • 7 个月前

Origami Robotics is building high-DOF robotic hands with in-joint motors and a co-designed data-collection glove to eliminate the embodiment gap by collecting high-quality, real-world data at scale. Congrats on the launch, Quanting Xie and Ryan Xie!
1:09

Origami Robotics is building high-DOF robotic hands with in-joint motors and a co-designed data-collection glove to eliminate the embodiment gap by collecting high-quality, real-world data at scale. Congrats on the launch, Quanting Xie and Ryan Xie!

Y Combinator

88,609 次观看 • 4 个月前

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)
0:41

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)

Abhishek Gupta

13,346 次观看 • 1 年前

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork
0:20

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

185,867 次观看 • 3 个月前

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:
0:29

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,804 次观看 • 5 个月前

Co-CEO Weekly Update 045 FF continues expanding the real-world deployment of its EAI Robotics ecosystem with new deliveries and scenario expansion. ① FF completed delivery of the Master robot and pilot delivery of the Aegis robot to Texas-based NS Federation, expanding real-world EAI Robotics deployment. ② The delivery introduces two new “Robot + Vehicle +” application scenarios: education and performance, backed by real market demand. ③ In “Robot & Vehicle + Education,” EAI robots support robotics training, research assistance, data collection, and hands-on AI education across schools, labs, and research institutions. ④ In “Robot & Vehicle + Performance,” robots serve as interactive performers for events, competitions, and entertainment environments—unlocking new consumer and light commercial markets. ⑤ On the EAI EV side, FF leadership held discussions in Washington, D.C. with the U.S. Department of Transportation and Department of Commerce on tariff policy, industry development, and technology innovation. #FFAI #AIXC #Robotics #FaradayFuture
8:29

Co-CEO Weekly Update 045 FF continues expanding the real-world deployment of its EAI Robotics ecosystem with new deliveries and scenario expansion. ① FF completed delivery of the Master robot and pilot delivery of the Aegis robot to Texas-based NS Federation, expanding real-world EAI Robotics deployment. ② The delivery introduces two new “Robot + Vehicle +” application scenarios: education and performance, backed by real market demand. ③ In “Robot & Vehicle + Education,” EAI robots support robotics training, research assistance, data collection, and hands-on AI education across schools, labs, and research institutions. ④ In “Robot & Vehicle + Performance,” robots serve as interactive performers for events, competitions, and entertainment environments—unlocking new consumer and light commercial markets. ⑤ On the EAI EV side, FF leadership held discussions in Washington, D.C. with the U.S. Department of Transportation and Department of Commerce on tariff policy, industry development, and technology innovation. #FFAI #AIXC #Robotics #FaradayFuture

YT Jia 贾跃亭

16,241 次观看 • 3 个月前

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)
1:15

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)

Zhenyu Jiang

44,263 次观看 • 1 年前