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China's largest humanoid robot data training center. Building by the Beijing Shijingshan District government and Leju Robotics Spanning over 10,000m² cover 100 high-frequency scenarios, including industrial manufacturing, commercial services, and home applications. 6 million high-quality data points annually.

CyberRobo

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61,637 просмотров • 10 месяцев назад •via X (Twitter)

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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.

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 просмотров • 10 месяцев назад

Leju Robotics has launched the upgraded KUAVO 5/5-W humanoid and wheeled robots, designed to meet the rigorous demands of industrial automation. The robots feature a modular design that is key to flexibility: the legs are quickly replaceable with bipedal configurations (for complex terrain) or wheeled setups (for long-distance stability). The hands are also modular, supporting dexterous hands, grippers, or claws. This adaptability, combined with a rotatable and height-adjustable body, allows the robot to handle a vast range of factory tasks. Built for industrial stamina, the KUAVO series supports a 20kg maximum payload and an upper-body dual-arm payload of 10kg. It is engineered for continuous operation, boasting a battery life of over 8 hours. The control system achieves "silky-smooth" precision via doubled joint communication frequency. To drive high generalizability, Leju relies on a data-driven model. It established China's largest data acquisition center in Beijing (referred to as the "largest humanoid robot skill school") to collect over 6 million data points annually from 1:1 replications of real industrial scenes. This data feeds a universal multi-modal dataset, enabling the robot's "intelligent brain" to continuously learn complex tasks through closed-loop feedback in client factory environments.

Leju Robotics has launched the upgraded KUAVO 5/5-W humanoid and wheeled robots, designed to meet the rigorous demands of industrial automation. The robots feature a modular design that is key to flexibility: the legs are quickly replaceable with bipedal configurations (for complex terrain) or wheeled setups (for long-distance stability). The hands are also modular, supporting dexterous hands, grippers, or claws. This adaptability, combined with a rotatable and height-adjustable body, allows the robot to handle a vast range of factory tasks. Built for industrial stamina, the KUAVO series supports a 20kg maximum payload and an upper-body dual-arm payload of 10kg. It is engineered for continuous operation, boasting a battery life of over 8 hours. The control system achieves "silky-smooth" precision via doubled joint communication frequency. To drive high generalizability, Leju relies on a data-driven model. It established China's largest data acquisition center in Beijing (referred to as the "largest humanoid robot skill school") to collect over 6 million data points annually from 1:1 replications of real industrial scenes. This data feeds a universal multi-modal dataset, enabling the robot's "intelligent brain" to continuously learn complex tasks through closed-loop feedback in client factory environments.

RoboHub🤖

34,676 просмотров • 9 месяцев назад

The "sim-to-real" gap just got a massive 2,300m² bridge. 🤖🏗️ NEURA Robotics and TUM are launching the TUM RoboGym at Munich Airport—Europe’s largest training center for Physical AI. Starting mid-2026, a fleet of humanoid robots will begin real-world training to fuel the Neuraverse ecosystem with high-quality, sovereign data.

The "sim-to-real" gap just got a massive 2,300m² bridge. 🤖🏗️ NEURA Robotics and TUM are launching the TUM RoboGym at Munich Airport—Europe’s largest training center for Physical AI. Starting mid-2026, a fleet of humanoid robots will begin real-world training to fuel the Neuraverse ecosystem with high-quality, sovereign data.

Humanoids daily

25,560 просмотров • 5 месяцев назад

The Beijing Innovation Center of Humanoid Robotics (X-Humanoid) is building its own "data factory" to tackle the biggest bottleneck in Embodied AI, aiming for a world-first 1-million-hour milestone of high-quality real-world data. 🤖 This 53,820-square-foot (5,000 square meters) facility is already operating with a fleet of over 120 robots from various makers, including their own Tien Kung series and platforms like Unitree and UBTECH. By replicating 30+ real-life scenarios—from kitchens to industrial lines—the center is capturing the "physical intuition" that digital simulations simply cannot replicate. Their "factory-style" pipeline ensures a 95% data pass rate through a rigorous three-stage inspection process, moving beyond the era of small-scale, "fine-tuned" lab datasets. With over 2 million downloads of their open-source Robomind dataset, they are quickly becoming the backbone for developers looking to bridge the sim-to-real gap.

The Beijing Innovation Center of Humanoid Robotics (X-Humanoid) is building its own "data factory" to tackle the biggest bottleneck in Embodied AI, aiming for a world-first 1-million-hour milestone of high-quality real-world data. 🤖 This 53,820-square-foot (5,000 square meters) facility is already operating with a fleet of over 120 robots from various makers, including their own Tien Kung series and platforms like Unitree and UBTECH. By replicating 30+ real-life scenarios—from kitchens to industrial lines—the center is capturing the "physical intuition" that digital simulations simply cannot replicate. Their "factory-style" pipeline ensures a 95% data pass rate through a rigorous three-stage inspection process, moving beyond the era of small-scale, "fine-tuned" lab datasets. With over 2 million downloads of their open-source Robomind dataset, they are quickly becoming the backbone for developers looking to bridge the sim-to-real gap.

RoboHub🤖

23,072 просмотров • 4 месяцев назад

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

TBPN

18,811 просмотров • 1 год назад

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.

Brandon Ong

164,607 просмотров • 10 дней назад

🔥 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 structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🔥 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 structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 просмотров • 4 месяцев назад

VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

RoboHub🤖

12,961 просмотров • 9 месяцев назад

This March, #BloombergInvest will bring together the most influential voices across asset management, banking, private capital and wealth to address the key issues shaping markets, including AI-driven investment strategies, data transparency and the future of financial infrastructure. Powered by Bloomberg's high-quality data and one of the world’s largest newsrooms, Bloomberg Invest delivers timely, actionable insight to the forces redefining global finance in the heart of New York's Financial District.

This March, #BloombergInvest will bring together the most influential voices across asset management, banking, private capital and wealth to address the key issues shaping markets, including AI-driven investment strategies, data transparency and the future of financial infrastructure. Powered by Bloomberg's high-quality data and one of the world’s largest newsrooms, Bloomberg Invest delivers timely, actionable insight to the forces redefining global finance in the heart of New York's Financial District.

Bloomberg

64,732 просмотров • 6 месяцев назад

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:

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

332,238 просмотров • 2 лет назад

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

Physics AI 🌊 Jensen just unveiled the NVIDIA Isaac GR00T Reference Humanoid Robot at GTC Taipei--the world’s first open-source humanoid robot reference design Built on Jetson Thor edge computing and the Isaac GR00T open platform, it features a Unitree H2 Plus body with Sharpa five-finger dexterous hands and 75 degrees of freedom. Even before Physical AI truly explodes, NVIDIA has built a complete end-to-end full-stack infrastructure: >chips (Jetson Thor + Blackwell), >synthetic data (GR00T-Dreams), >world models (Cosmos), >foundation models (GR00T N-series VLA), >high-fidelity simulation platforms (Isaac Sim + Isaac Lab + Omniverse). Dozens of humanoid robot companies ,including Agility Robotics, Aglie robotics,Boston Dynamics, Figure, 1X, NEURA Robotics, XPENG Robotics, and more are already running large-scale daily simulation training on this platform, rapidly iterating perception, reasoning, and actions in virtual environments, speeding up the jump from lab prototypes to real factory deployment. By providing an open reference design and full-stack tools, NVIDIA has dramatically lowered the barrier, letting every robotics team join the Physical AI wave with low cost and high efficiency.

CyberRobo

17,092 просмотров • 2 месяцев назад

This robot knows who won the World Cup! "Xiaozhi, Xiaozhi, who won the 2026 FIFA World Cup?" Alvaro Lago, from Spain, gave this "pop quiz" to a robot at the Embodied AI Robot Experience Center in Changzhou, east China's Jiangsu Province. "The 2026 World Cup Champions are Spain!" the robot blurted out instantly, drawing an enthusiastic thumbs-up from the Spanish native. From understanding football to offering services and interacting with people, what's on display is more than just robots, it's a showcase of China's manufacturing strength. The experience center brings together 36 companies and 104 exhibits, spanning core components, intelligent systems and complete machine integration, offering a full panorama of the embodied intelligence industry chain. The Wujin National Hi-tech Industrial Zone, site of the experience center, is already home to more than 120 robotics companies and is accelerating efforts to build a globally competitive robotics industry hub. As China's robots race toward the future, one thing is clear: when it comes to robotics, Changzhou is a city you don't want to miss.

This robot knows who won the World Cup! "Xiaozhi, Xiaozhi, who won the 2026 FIFA World Cup?" Alvaro Lago, from Spain, gave this "pop quiz" to a robot at the Embodied AI Robot Experience Center in Changzhou, east China's Jiangsu Province. "The 2026 World Cup Champions are Spain!" the robot blurted out instantly, drawing an enthusiastic thumbs-up from the Spanish native. From understanding football to offering services and interacting with people, what's on display is more than just robots, it's a showcase of China's manufacturing strength. The experience center brings together 36 companies and 104 exhibits, spanning core components, intelligent systems and complete machine integration, offering a full panorama of the embodied intelligence industry chain. The Wujin National Hi-tech Industrial Zone, site of the experience center, is already home to more than 120 robotics companies and is accelerating efforts to build a globally competitive robotics industry hub. As China's robots race toward the future, one thing is clear: when it comes to robotics, Changzhou is a city you don't want to miss.

People's Daily, China

28,786 просмотров • 27 дней назад

What is the best data for training humanoid & robotics foundation models? Pete Florence Pete Florence (CEO Eric Nehrlich, ex-Google DeepMind) dropped his live data tier list in this 7-minute clip on TBPN: - S-tier: Real-world robot experience (especially glove/sensor high-dexterity data) - A/B-tier: Internet/YouTube videos. Surprisingly powerful for transfer learning (the “web data” moment for physical AI) - B-tier: Text/common crawl (Reddit, books, etc.). Useful priors, but not enough alone - C-tier: Motion Capture. Great for whole-body motion, weak on finger dexterity - C or lower: Simulation / synthetic / world models. High potential, still waiting for strong real-world proof Generalist has collected 270,000+ hours of real-world manipulation data (scaling ~10k hours/week). And Pete stressed one key point: “The quality of data is incredibly important.” It’s not just about volume. It’s developing intuition for what actually drives performance through hands-on work. As Physical AI scales, curated real-world and high-quality internet video looks like a winning combo. h/t YUJI | AI Robotics

What is the best data for training humanoid & robotics foundation models? Pete Florence Pete Florence (CEO Eric Nehrlich, ex-Google DeepMind) dropped his live data tier list in this 7-minute clip on TBPN: - S-tier: Real-world robot experience (especially glove/sensor high-dexterity data) - A/B-tier: Internet/YouTube videos. Surprisingly powerful for transfer learning (the “web data” moment for physical AI) - B-tier: Text/common crawl (Reddit, books, etc.). Useful priors, but not enough alone - C-tier: Motion Capture. Great for whole-body motion, weak on finger dexterity - C or lower: Simulation / synthetic / world models. High potential, still waiting for strong real-world proof Generalist has collected 270,000+ hours of real-world manipulation data (scaling ~10k hours/week). And Pete stressed one key point: “The quality of data is incredibly important.” It’s not just about volume. It’s developing intuition for what actually drives performance through hands-on work. As Physical AI scales, curated real-world and high-quality internet video looks like a winning combo. h/t YUJI | AI Robotics

Michelle Sun

15,351 просмотров • 2 месяцев назад

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 просмотров • 7 месяцев назад

China's humanoid robotics market is on fire. With orders expected to top 30,000 units this year—a tenfold jump from 2024's total of less than 3,000—2025 is officially shaping up to be the "Year of Mass Production." This surge, driven by an expansion into new sectors like industrial manufacturing, logistics, and elder care, is reflected in a wave of new deals across the industry. Here's a look at some of the key commercial progress: Astribot: A 1,000-unit order for industrial and logistics deployment over two years. TianTai Robotics: Signed a major 10,000-unit order for caregiving robots. Noetix Robotics : Received over 2,000 intent orders in one month, valued at over 100 million yuan, with a focus on education and commercial performances. AgiBot: Expects to ship thousands of units this year and tens of thousands in 2026. Unitree Robotics: Has orders for thousands of units and is one of the most visible products in the industry. UBTech: Aims to deliver 500 industrial humanoids in 2025, with educational robot orders already exceeding 300 units. Robot Era: Delivered over 300 units by July 2025 with 500 more on hand. TLIBOT: Has around 1,000 intent orders. Galbot: Secured orders for its supermarket security robot, Galbot, in 100 stores. AI² Robotics: Has nearly 500 orders for its general-purpose robots for industrial and public service scenarios. But here’s the crucial reality check. While the order boom is exciting, it doesn't automatically translate to fulfilled deliveries. Many companies lack the production capacity to keep up. A significant portion of these are "intent orders" or framework agreements, not guaranteed sales. Furthermore, the market is heavily B2B-focused, with consumer demand representing only about 5% of sales. Some orders are even symbolic, for public relations or strategic purposes. This “order frenzy” is a starting point, not the finish line. The true test for China's humanoid robot industry isn't who can secure the biggest order, but who can consistently deliver on it and build a stable market for the future.

China's humanoid robotics market is on fire. With orders expected to top 30,000 units this year—a tenfold jump from 2024's total of less than 3,000—2025 is officially shaping up to be the "Year of Mass Production." This surge, driven by an expansion into new sectors like industrial manufacturing, logistics, and elder care, is reflected in a wave of new deals across the industry. Here's a look at some of the key commercial progress: Astribot: A 1,000-unit order for industrial and logistics deployment over two years. TianTai Robotics: Signed a major 10,000-unit order for caregiving robots. Noetix Robotics : Received over 2,000 intent orders in one month, valued at over 100 million yuan, with a focus on education and commercial performances. AgiBot: Expects to ship thousands of units this year and tens of thousands in 2026. Unitree Robotics: Has orders for thousands of units and is one of the most visible products in the industry. UBTech: Aims to deliver 500 industrial humanoids in 2025, with educational robot orders already exceeding 300 units. Robot Era: Delivered over 300 units by July 2025 with 500 more on hand. TLIBOT: Has around 1,000 intent orders. Galbot: Secured orders for its supermarket security robot, Galbot, in 100 stores. AI² Robotics: Has nearly 500 orders for its general-purpose robots for industrial and public service scenarios. But here’s the crucial reality check. While the order boom is exciting, it doesn't automatically translate to fulfilled deliveries. Many companies lack the production capacity to keep up. A significant portion of these are "intent orders" or framework agreements, not guaranteed sales. Furthermore, the market is heavily B2B-focused, with consumer demand representing only about 5% of sales. Some orders are even symbolic, for public relations or strategic purposes. This “order frenzy” is a starting point, not the finish line. The true test for China's humanoid robot industry isn't who can secure the biggest order, but who can consistently deliver on it and build a stable market for the future.

RoboHub🤖

199,146 просмотров • 11 месяцев назад

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,835 просмотров • 8 месяцев назад

🏢 Weekly Data Center Update! This week, we achieved a major milestone with the completion of an advanced underground gigabit fiber optic installation at our data center. 🔹 This state-of-the-art network integrates all major Internet Service Providers in Romania, ensuring dedicated bandwidth with exceptional internet speed and reliability for residential and business users alike. 🔹 Additionally, the infrastructure features a cutting-edge Dark Fiber connection, enabling AI TECH to handle specialized, high-capacity data transitions. This robust setup is designed to support data-intensive applications, including AI systems, cloud computing, and seamless communication networks. 🔹 By implementing this advanced connectivity solution, we’re not just enhancing performance, we’re reinforcing our commitment to remaining at the forefront of High Performance Computing. ✨ Stay tuned for more updates as we continue building the future of AI-driven technology.

🏢 Weekly Data Center Update! This week, we achieved a major milestone with the completion of an advanced underground gigabit fiber optic installation at our data center. 🔹 This state-of-the-art network integrates all major Internet Service Providers in Romania, ensuring dedicated bandwidth with exceptional internet speed and reliability for residential and business users alike. 🔹 Additionally, the infrastructure features a cutting-edge Dark Fiber connection, enabling AI TECH to handle specialized, high-capacity data transitions. This robust setup is designed to support data-intensive applications, including AI systems, cloud computing, and seamless communication networks. 🔹 By implementing this advanced connectivity solution, we’re not just enhancing performance, we’re reinforcing our commitment to remaining at the forefront of High Performance Computing. ✨ Stay tuned for more updates as we continue building the future of AI-driven technology.

AITECH CLOUD NETWORK

55,185 просмотров • 1 год назад