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Introducing TRLC-DK1-X: An accessible humanoid data collection platform. It features two 6+1 DOF leader-follower arm pairs and three ultra-wide FOV cameras, integrated for learning static, bimanual manipulation tasks. Order now for $4,999.

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Jannik Grothusen

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Not every robot needs a humanlike head. 🇰🇷🤖 South Korea’s XYZ unveiled DEUX, a dual-arm semi-humanoid built for everyday spaces. DEUX pairs two 7-DoF arms with two 7-DoF three-finger hands, a swerve-drive base, and an 11 kg dual-arm payload. XYZ already builds robot baristas and indoor delivery robots. Its Physical AI pipeline runs from Glove X data collection to model training, robot deployment, and real-world validation. Keep the arms and mobility. Skip the anatomy you don’t need.
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Not every robot needs a humanlike head. 🇰🇷🤖 South Korea’s XYZ unveiled DEUX, a dual-arm semi-humanoid built for everyday spaces. DEUX pairs two 7-DoF arms with two 7-DoF three-finger hands, a swerve-drive base, and an 11 kg dual-arm payload. XYZ already builds robot baristas and indoor delivery robots. Its Physical AI pipeline runs from Glove X data collection to model training, robot deployment, and real-world validation. Keep the arms and mobility. Skip the anatomy you don’t need.

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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.
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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🤖

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X-Humanoid unveiled TG-VLA, which it calls the world's first whole-body VLA framework for full-size humanoid robots. The goal: move humanoids past "mobile dual-arm systems" to human-like, whole-body coordinated operation. Built on three technologies: - HEX: an open-source full-size humanoid VLA that learns across embodiments and coordinates the whole body. Claimed SOTA on real-world whole-body manipulation - HAF-VLA: hierarchical action flow that decomposes whole-body motion into subtasks. Best or joint-best on 6 benchmarks - DSRL-DCT: online RL for high-DoF humanoids in a compressed latent space. 100% success on mobile manipulation HEX is open-source.
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X-Humanoid unveiled TG-VLA, which it calls the world's first whole-body VLA framework for full-size humanoid robots. The goal: move humanoids past "mobile dual-arm systems" to human-like, whole-body coordinated operation. Built on three technologies: - HEX: an open-source full-size humanoid VLA that learns across embodiments and coordinates the whole body. Claimed SOTA on real-world whole-body manipulation - HAF-VLA: hierarchical action flow that decomposes whole-body motion into subtasks. Best or joint-best on 6 benchmarks - DSRL-DCT: online RL for high-DoF humanoids in a compressed latent space. 100% success on mobile manipulation HEX is open-source.

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Booster T2 is now available. Smart. Powerful. Built to Perform. As a new flagship embodied development platform, Booster T2 delivers up to 2070 TFLOPS of computing power, the highest in its class for bipedal humanoid robots. Combined with a high-output, high-DOF body, it enables sharper motion perception, stronger understanding, and reliable execution in complex scenarios. From real-time perception to rapid response, from agile movement to precise manipulation, Booster T2 combines powerful computing performance and reliable hardware with Booster Studio's integrated development platform, unlocking limitless possibilities for developers. Search "Booster Robotics" to visit the official website and order now.
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Booster T2 is now available. Smart. Powerful. Built to Perform. As a new flagship embodied development platform, Booster T2 delivers up to 2070 TFLOPS of computing power, the highest in its class for bipedal humanoid robots. Combined with a high-output, high-DOF body, it enables sharper motion perception, stronger understanding, and reliable execution in complex scenarios. From real-time perception to rapid response, from agile movement to precise manipulation, Booster T2 combines powerful computing performance and reliable hardware with Booster Studio's integrated development platform, unlocking limitless possibilities for developers. Search "Booster Robotics" to visit the official website and order now.

Booster Robotics

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Reporting from CES 2026 LG Electronics has unveiled LG CLOiD, an AI-powered home robot, advancing its “Zero Labor Home” vision to automate chores. CLOiD features: • Wheeled base for safe navigation • Tilting torso • Two 7-DoF arms with 5-fingered hands for precise manipulation • Head as mobile AI hub equipped with a chipset (its brain), display, speakers, cameras, sensors, and voice-based generative AI for communication, learning user patterns, and appliance control Powered by LG’s Physical AI (VLM for understanding + VLA for actions), trained on tens of thousands of hours of household task data. It performs tasks like breakfast prep, laundry (loading/folding), dishwasher unloading, and more. LG also launched AXIUM actuators lineup for efficient robotics solutions. Vision for the future: AI-driven homes freeing time for meaningful activities.
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Reporting from CES 2026 LG Electronics has unveiled LG CLOiD, an AI-powered home robot, advancing its “Zero Labor Home” vision to automate chores. CLOiD features: • Wheeled base for safe navigation • Tilting torso • Two 7-DoF arms with 5-fingered hands for precise manipulation • Head as mobile AI hub equipped with a chipset (its brain), display, speakers, cameras, sensors, and voice-based generative AI for communication, learning user patterns, and appliance control Powered by LG’s Physical AI (VLM for understanding + VLA for actions), trained on tens of thousands of hours of household task data. It performs tasks like breakfast prep, laundry (loading/folding), dishwasher unloading, and more. LG also launched AXIUM actuators lineup for efficient robotics solutions. Vision for the future: AI-driven homes freeing time for meaningful activities.

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What if a dexterous robot could learn a 20+ step chemistry experiment without a single on-robot training demo? Meet TwinDEX: a pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment. The twinned design shares identical kinematics, contact surfaces, visual appearance, and sensors across collection and deployment — keeping observations and actions aligned end to end. Trained from scratch on only a few hundred wearable demonstrations - with zero on-robot training or intervention data - TwinDEX completed a standardized chemistry experiment involving tool switches, fine force control, and bimanual coordination. Robot-free data showed comparable learning efficiency on the multi-task evaluation, TwinDEX delivered 5.3 times effective throughput than on-robot teleoperation. TwinDEX demonstrates that high-quality robot-free data can fully substitute for on-robot teleoperation data on challenging dexterous tasks — removing the dependency on real-robot hardware that has been the central bottleneck to scaling dexterous manipulation data. This was the proof-it phase. Now comes scale: what emerges at tens of thousands, or millions, of episodes? Watch the demo and read the technical blog: #TwinDEX #Robotics #EmbodiedAI #DexterousManipulation
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What if a dexterous robot could learn a 20+ step chemistry experiment without a single on-robot training demo? Meet TwinDEX: a pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment. The twinned design shares identical kinematics, contact surfaces, visual appearance, and sensors across collection and deployment — keeping observations and actions aligned end to end. Trained from scratch on only a few hundred wearable demonstrations - with zero on-robot training or intervention data - TwinDEX completed a standardized chemistry experiment involving tool switches, fine force control, and bimanual coordination. Robot-free data showed comparable learning efficiency on the multi-task evaluation, TwinDEX delivered 5.3 times effective throughput than on-robot teleoperation. TwinDEX demonstrates that high-quality robot-free data can fully substitute for on-robot teleoperation data on challenging dexterous tasks — removing the dependency on real-robot hardware that has been the central bottleneck to scaling dexterous manipulation data. This was the proof-it phase. Now comes scale: what emerges at tens of thousands, or millions, of episodes? Watch the demo and read the technical blog: #TwinDEX #Robotics #EmbodiedAI #DexterousManipulation

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Another headless wheeled humanoid Shenzhen VLAI Robotics just released a pretty pragmatic humanoid robot called L1. I checked the price, starting at RMB 28,800($4,188). wheeled base + height-adjustable dual arms.Anyone can be built into a household assistant:fetching water, tidying up the kitchen and living room, doing laundry, and more. >Dual arms with 16 DOF, bionic-style structure, capable of relatively fine tasks like grasping and plug-in operations >Adjustable arm height (~70–160 cm), covering both tabletop and standing work >Differential-drive wheeled base, 100 kg payload, simple but well-suited for indoor use, supports working while moving >Open interfaces + teleoperation/data collection. L1 feels more like a development platform than a finished product Beyond household humanoids, developers can build specialized humanoid robots tailored to a wide range of scenarios,from scientific research and manufacturing to warehousing, retail, and caregiving... We’re already seeing quite a few robots in this form factor. While some are pushing toward full humanoids, this “less-human” approach might actually reach real-world deployment sooner. --- (Shenzhen truly is a home for humanoid robots,it seems as though a new humanoid robot company could spring into existence at any moment.）
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Another headless wheeled humanoid Shenzhen VLAI Robotics just released a pretty pragmatic humanoid robot called L1. I checked the price, starting at RMB 28,800($4,188). wheeled base + height-adjustable dual arms.Anyone can be built into a household assistant:fetching water, tidying up the kitchen and living room, doing laundry, and more. >Dual arms with 16 DOF, bionic-style structure, capable of relatively fine tasks like grasping and plug-in operations >Adjustable arm height (~70–160 cm), covering both tabletop and standing work >Differential-drive wheeled base, 100 kg payload, simple but well-suited for indoor use, supports working while moving >Open interfaces + teleoperation/data collection. L1 feels more like a development platform than a finished product Beyond household humanoids, developers can build specialized humanoid robots tailored to a wide range of scenarios,from scientific research and manufacturing to warehousing, retail, and caregiving... We’re already seeing quite a few robots in this form factor. While some are pushing toward full humanoids, this “less-human” approach might actually reach real-world deployment sooner. --- (Shenzhen truly is a home for humanoid robots,it seems as though a new humanoid robot company could spring into existence at any moment.）

CyberRobo

38,700 Aufrufe • vor 5 Monaten

🔥 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 →
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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 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 Aufrufe • vor 5 Monaten

Holy… is this really not a human? That was my reaction too,I could hardly believe it. This is an ultra-humanlike humanoid robot. 1.73 m tall, 70 kg, with 115 degrees of freedom across the body,36 in the hands alone,and full-body tactile skin. … This is KAI, the first humanoid robot from Shenzhen-based KinetixAI. It’s essentially a 1:1 human replica，except it has no face. I wonder if it could use Aheadform’s M1? that will be interesting. Powered by its KAI World Model and trained on large-scale Engocentric data, it can fold clothes, use tools, handle deliveries and takeout, and assist with childcare,covering a wide range of household tasks. Ok, expectations for Tesla Optimus v3 have been raised once again.
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Holy… is this really not a human? That was my reaction too,I could hardly believe it. This is an ultra-humanlike humanoid robot. 1.73 m tall, 70 kg, with 115 degrees of freedom across the body,36 in the hands alone,and full-body tactile skin. … This is KAI, the first humanoid robot from Shenzhen-based KinetixAI. It’s essentially a 1:1 human replica，except it has no face. I wonder if it could use Aheadform’s M1? that will be interesting. Powered by its KAI World Model and trained on large-scale Engocentric data, it can fold clothes, use tools, handle deliveries and takeout, and assist with childcare,covering a wide range of household tasks. Ok, expectations for Tesla Optimus v3 have been raised once again.

CyberRobo

109,784 Aufrufe • vor 4 Monaten

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:
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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

32,025 Aufrufe • vor 7 Monaten

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.
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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🤖

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Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.
2:35

Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.

AI at Meta

453,343 Aufrufe • vor 1 Jahr

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗
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🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗

Hedgie

162,060 Aufrufe • vor 3 Monaten

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:34

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

60,985 Aufrufe • vor 7 Monaten

Humanoid Marathon Champion "TienKung" Enters the Factory At IROS 2025, the Beijing Humanoid Robot Innovation Center (BHRIC) and UBTECH officially launched the Software Development Kit (SDK) for their general-purpose embodied intelligence platform, "HuiSiKaiWu." This move signals a significant step towards an open-source embodied AI ecosystem. The HuiSiKaiWu platform is designed for ease of use and low-threshold deployment, enabling multi-agent collaboration (one brain, multiple functions/robots). The core "brain" uses a dual-model architecture: the Pelican VLM and the WoW World Model, driving autonomous learning and decision-making. The "cerebellum" includes the cross-body XR-1 VLA Model, which has shown strong performance in rapid, few-shot skill transfer across tasks. BHRIC simultaneously announced its first industrial application: since September 2025, the "TienKung 2.0" and "Tianyi 2.0" humanoids have been deployed at the Foton Cummins engine factory. They autonomously handle material bin fetching and transportation on the "unmanned production line," adapting to various goods and shelf heights. Foton Cummins highlighted the robots' high stability and generalization potential in complex industrial processes. Beyond industrial work, BHRIC showcased other applications: the high-performance "TienKung Ultra" is being used at the Li-Ning Sports Science Lab for running shoe tests, precisely simulating human gaits. The platform is expanding applications across manufacturing, logistics, and sports science. The HuiSiKaiWu SDK is now available for collaborative trials by research institutions and development teams, offering a full toolchain for skill calling and scene deployment. BHRIC plans to gradually open-source its core algorithms, including VLM and VLA models, and hosted the "X-Humanoid Young Talent Meetup" and the "2026 Re-Action Humanoid Robot Challenge" to build a comprehensive developer ecosystem.
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Humanoid Marathon Champion "TienKung" Enters the Factory At IROS 2025, the Beijing Humanoid Robot Innovation Center (BHRIC) and UBTECH officially launched the Software Development Kit (SDK) for their general-purpose embodied intelligence platform, "HuiSiKaiWu." This move signals a significant step towards an open-source embodied AI ecosystem. The HuiSiKaiWu platform is designed for ease of use and low-threshold deployment, enabling multi-agent collaboration (one brain, multiple functions/robots). The core "brain" uses a dual-model architecture: the Pelican VLM and the WoW World Model, driving autonomous learning and decision-making. The "cerebellum" includes the cross-body XR-1 VLA Model, which has shown strong performance in rapid, few-shot skill transfer across tasks. BHRIC simultaneously announced its first industrial application: since September 2025, the "TienKung 2.0" and "Tianyi 2.0" humanoids have been deployed at the Foton Cummins engine factory. They autonomously handle material bin fetching and transportation on the "unmanned production line," adapting to various goods and shelf heights. Foton Cummins highlighted the robots' high stability and generalization potential in complex industrial processes. Beyond industrial work, BHRIC showcased other applications: the high-performance "TienKung Ultra" is being used at the Li-Ning Sports Science Lab for running shoe tests, precisely simulating human gaits. The platform is expanding applications across manufacturing, logistics, and sports science. The HuiSiKaiWu SDK is now available for collaborative trials by research institutions and development teams, offering a full toolchain for skill calling and scene deployment. BHRIC plans to gradually open-source its core algorithms, including VLM and VLA models, and hosted the "X-Humanoid Young Talent Meetup" and the "2026 Re-Action Humanoid Robot Challenge" to build a comprehensive developer ecosystem.

RoboHub🤖

39,337 Aufrufe • vor 10 Monaten

Italy's IIT really has so many humanoid gems. Came across their 2018 Centauro--a centaur-like disaster response robot that combines a quadruped lower body with a humanoid torso and arms. Built for harsh settings like rubble fields or disaster zones, it needs both stable movement over uneven ground and the strength to use human tools for forceful tasks such as breaching doors or clearing obstacles. The four legs deliver terrain-following locomotion and stair climbing, while the upper body handles dexterous, high-force manipulation. The robot blends full-body teleoperation--mapping an operator’s movements directly to the machine, with local autonomous modules for motion and impedance control, supporting supervised or higher-level autonomy when useful. At 1.5m tall and 93kg, it carries roughly 11kg payload per arm with impact-resilient actuators. Battery life is about 2.5 hours. Its hybrid wheeled-legged legs (6 DoF each, with wheels at the ankles acting like rolling hooves) provide better stability than pure bipeds while keeping tool use intuitive. This focus on matching robot morphology to real task demands, rather than defaulting to a standard humanoid form, remains a useful reference. Curious what others think:could hybrid centaur-style platforms play a larger role in future rescue or specialized operations?
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Italy's IIT really has so many humanoid gems. Came across their 2018 Centauro--a centaur-like disaster response robot that combines a quadruped lower body with a humanoid torso and arms. Built for harsh settings like rubble fields or disaster zones, it needs both stable movement over uneven ground and the strength to use human tools for forceful tasks such as breaching doors or clearing obstacles. The four legs deliver terrain-following locomotion and stair climbing, while the upper body handles dexterous, high-force manipulation. The robot blends full-body teleoperation--mapping an operator’s movements directly to the machine, with local autonomous modules for motion and impedance control, supporting supervised or higher-level autonomy when useful. At 1.5m tall and 93kg, it carries roughly 11kg payload per arm with impact-resilient actuators. Battery life is about 2.5 hours. Its hybrid wheeled-legged legs (6 DoF each, with wheels at the ankles acting like rolling hooves) provide better stability than pure bipeds while keeping tool use intuitive. This focus on matching robot morphology to real task demands, rather than defaulting to a standard humanoid form, remains a useful reference. Curious what others think:could hybrid centaur-style platforms play a larger role in future rescue or specialized operations?

CyberRobo

41,894 Aufrufe • vor 1 Monat

Force-sensing fingers! 🧤 Stanford researchers just released UMI-FT, a handheld data collection platform that puts compact six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose data. Many manipulation tasks require careful force modulation: too little force and the task fails, too much and you cause damage. But commercial force/torque sensors are expensive, bulky, and fragile, which has limited large-scale force-aware policy learning. UMI-FT changes the economics. The platform uses an iPhone for RGB vision, ultrawide RGB, depth, and pose via ARKit, with each finger sensorized using a CoinFT sensor to capture per-finger wrench information during manipulation. This multimodal data trains an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. The learned policy runs slowest and generates reference targets, while model-based compliance and force controllers provide delicate 6D compliance control and real-time force modulation. They tested on three contact-rich, force-sensitive tasks: whiteboard wiping (locate eraser, grasp, wipe until clean), skewering zucchini (grasp slice firmly, push onto stick until punctured), and lightbulb insertion (grasp bulb, align bayonet pin with socket slit, insert while overcoming spring force, rotate to light up). The results are clear. Policies without compliance struggle to modulate contact force and trigger safety faults from excessive force. Policies without force sensing fail to grasp unseen objects or resist reaction forces, causing slippage. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Force-sensing fingers! 🧤 Stanford researchers just released UMI-FT, a handheld data collection platform that puts compact six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose data. Many manipulation tasks require careful force modulation: too little force and the task fails, too much and you cause damage. But commercial force/torque sensors are expensive, bulky, and fragile, which has limited large-scale force-aware policy learning. UMI-FT changes the economics. The platform uses an iPhone for RGB vision, ultrawide RGB, depth, and pose via ARKit, with each finger sensorized using a CoinFT sensor to capture per-finger wrench information during manipulation. This multimodal data trains an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. The learned policy runs slowest and generates reference targets, while model-based compliance and force controllers provide delicate 6D compliance control and real-time force modulation. They tested on three contact-rich, force-sensitive tasks: whiteboard wiping (locate eraser, grasp, wipe until clean), skewering zucchini (grasp slice firmly, push onto stick until punctured), and lightbulb insertion (grasp bulb, align bayonet pin with socket slit, insert while overcoming spring force, rotate to light up). The results are clear. Policies without compliance struggle to modulate contact force and trigger safety faults from excessive force. Policies without force sensing fail to grasp unseen objects or resist reaction forces, causing slippage. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

12,822 Aufrufe • vor 7 Monaten

This is how ALOHA's "teleoperation" system works - a fancy word for "remote control". Training robots will be more and more like playing games in the physical world. A human operates a "joystick++" to perform tasks and collect data, or intervene if there's any safety concern. There's actually a learning curve to master the controller, much like practicing gaming skills. Teleoperation can be done in many different ways. ALOHA is an impressive custom-built system with very low cost. Here're a few alternatives: (1) Motion Capture (MoCap): apply the MoCap systems used for Hollywood movies to capture the fine-grained motions of hand joints. There would be no "embodiment gap" if the robot hand has 5 fingers. For instance, a demonstrator can wear a CyberGlove ( and manipulate the objects. CyberGlove will capture the motion signals & haptic feedback in real-time, which can be re-targeted onto the humanoid. (2) Wearing gloves & markers can be clumsy. An alternative way to do MoCap is through computer vision. DexPilot from NVIDIA enables marker-less and glove-free data collection. The human operator simply uses their bare hands to perform the tasks. 4 Intel RealSense depth cameras and 2 NVIDIA Titan XP GPUs (yeah, 2019 work) translate the pixels to precise motion signals for robot learning. (3) VR Headset: turn the training room into a VR game and "role play" the robot. This has the advantage of scalable remote data collection - annotators from around the world can contribute without coming onsite. VR demonstration technique appeared in research projects like the iGibson home robot simulator, an initiative that I participated in at Stanford: Behind-the-scene video by Litian Liang
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This is how ALOHA's "teleoperation" system works - a fancy word for "remote control". Training robots will be more and more like playing games in the physical world. A human operates a "joystick++" to perform tasks and collect data, or intervene if there's any safety concern. There's actually a learning curve to master the controller, much like practicing gaming skills. Teleoperation can be done in many different ways. ALOHA is an impressive custom-built system with very low cost. Here're a few alternatives: (1) Motion Capture (MoCap): apply the MoCap systems used for Hollywood movies to capture the fine-grained motions of hand joints. There would be no "embodiment gap" if the robot hand has 5 fingers. For instance, a demonstrator can wear a CyberGlove ( and manipulate the objects. CyberGlove will capture the motion signals & haptic feedback in real-time, which can be re-targeted onto the humanoid. (2) Wearing gloves & markers can be clumsy. An alternative way to do MoCap is through computer vision. DexPilot from NVIDIA enables marker-less and glove-free data collection. The human operator simply uses their bare hands to perform the tasks. 4 Intel RealSense depth cameras and 2 NVIDIA Titan XP GPUs (yeah, 2019 work) translate the pixels to precise motion signals for robot learning. (3) VR Headset: turn the training room into a VR game and "role play" the robot. This has the advantage of scalable remote data collection - annotators from around the world can contribute without coming onsite. VR demonstration technique appeared in research projects like the iGibson home robot simulator, an initiative that I participated in at Stanford: Behind-the-scene video by Litian Liang

Jim Fan

124,783 Aufrufe • vor 2 Jahren

The Bitcoin Intelligence is About to Launch! We have something exciting to share. Next week, we are launching the ultimate Bitcoin analytics platform – Bitcoin Intelligence – which you have been asking for all along. It includes: 1- Bitcoin data & live charts with our models (Quantile Model, VPLI, Power Law, etc.) 2- Video analyses 3- Newsletter: The Bitcoin Intelligence Report All three will be accessible in one dashboard. And it covers updates on macroeconomics, on-chain data, price dynamics, and derivatives. After two decades of data modeling, a decade of studying Bitcoin, years of publishing Bitcoin analysis on social media, and many requests throughout last year, I am ready to take things to the next level. And I am not alone; we will be working with a team of top Bitcoin analysts to bring you the content you deserve. We’re soon opening early access, and we’d love for you to be among the first to experience it. Watch a short video introducing Bitcoin Intelligence. And find a link to the waitlist in the first comment below.
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The Bitcoin Intelligence is About to Launch! We have something exciting to share. Next week, we are launching the ultimate Bitcoin analytics platform – Bitcoin Intelligence – which you have been asking for all along. It includes: 1- Bitcoin data & live charts with our models (Quantile Model, VPLI, Power Law, etc.) 2- Video analyses 3- Newsletter: The Bitcoin Intelligence Report All three will be accessible in one dashboard. And it covers updates on macroeconomics, on-chain data, price dynamics, and derivatives. After two decades of data modeling, a decade of studying Bitcoin, years of publishing Bitcoin analysis on social media, and many requests throughout last year, I am ready to take things to the next level. And I am not alone; we will be working with a team of top Bitcoin analysts to bring you the content you deserve. We’re soon opening early access, and we’d love for you to be among the first to experience it. Watch a short video introducing Bitcoin Intelligence. And find a link to the waitlist in the first comment below.

Sina 🗝️⚡ BI Report

42,596 Aufrufe • vor 1 Jahr