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Should humanoids have tails? Current legged robots struggle with center of mass control and quick stabilization. Adding more DOF to legs gets complicated fast. Nature already solved this: Cheetahs, kangaroos, dinosaurs – they use tails for dynamic balance, sharp turns, and recovery from unexpected shifts. The robotics insight? Let...

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Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.
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Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

RoboHub🤖

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In 1980, Marc Raibert started the Leg Lab at Carnegie Mellon with a fundamental question: ⚖️ What if balance comes from movement, not stillness? At the time, robots were bolted down. Motion meant precision and repeatability. The idea was to build deterministic robots that found stability in their predictability. Their key insight - nature doesn't work that way, why should robots? ⚙️ What they built: Robots that bounced and stumbled but stayed upright through dynamic motion, not static stability. It looked awkward. It wasn't immediately useful. But it worked. Every hop, every stumble, every recovery taught them how dynamic balance actually works. 🎯 The result: Raibert spun out Boston Dynamics (1992), that DNA came with him. BigDog, Atlas, Spot – all descendants of those early hopping machines. Every humanoid today: Figure, Tesla, Agility - uses principles discovered in the Leg Lab. Dynamic stability. Active balance. Movement as intelligence.
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In 1980, Marc Raibert started the Leg Lab at Carnegie Mellon with a fundamental question: ⚖️ What if balance comes from movement, not stillness? At the time, robots were bolted down. Motion meant precision and repeatability. The idea was to build deterministic robots that found stability in their predictability. Their key insight - nature doesn't work that way, why should robots? ⚙️ What they built: Robots that bounced and stumbled but stayed upright through dynamic motion, not static stability. It looked awkward. It wasn't immediately useful. But it worked. Every hop, every stumble, every recovery taught them how dynamic balance actually works. 🎯 The result: Raibert spun out Boston Dynamics (1992), that DNA came with him. BigDog, Atlas, Spot – all descendants of those early hopping machines. Every humanoid today: Figure, Tesla, Agility - uses principles discovered in the Leg Lab. Dynamic stability. Active balance. Movement as intelligence.

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Running legged robots with AI. Today we met @swiss_mile, the team connecting AI with the physical world using autonomous machines. An ideal mobile robot needs to move quickly, overcoming obstacles while carrying heavy loads—something current robots still struggle with. Many robots haven't been widely adopted due to issues with real-world problem-solving. To address this, the Swiss-mile team have created a solution that combines robots with wheels and legs, powered by advanced AI. These robots can walk, drive, stand, open doors, and even handle packages. By integrating these features, the team are on a mission to push automation forward beyond stationary robotic arms and have raised $22m in Seed Funding from Jeff Bezos, HongShan, Amazon Industrial Innovation Fund and Armada Investment to do so.
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Running legged robots with AI. Today we met @swiss_mile, the team connecting AI with the physical world using autonomous machines. An ideal mobile robot needs to move quickly, overcoming obstacles while carrying heavy loads—something current robots still struggle with. Many robots haven't been widely adopted due to issues with real-world problem-solving. To address this, the Swiss-mile team have created a solution that combines robots with wheels and legs, powered by advanced AI. These robots can walk, drive, stand, open doors, and even handle packages. By integrating these features, the team are on a mission to push automation forward beyond stationary robotic arms and have raised $22m in Seed Funding from Jeff Bezos, HongShan, Amazon Industrial Innovation Fund and Armada Investment to do so.

Circuit

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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,090 Aufrufe • vor 8 Monaten

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:
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I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

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THEY BUILT A COMBAT-GRADE EXOSKELETON. WATCH HOW IT MOVES. 🤖🩻 Dark green, “S-01” stamped across the chest plate, built with the kind of bulk you’d expect on a battlefield platform. This is a new exoskeleton prototype reportedly developed for the U.S. military, and in this clip it’s shifting its weight foot to foot, testing balance and stance in a parking lot outside what looks like an industrial facility. This is the part nobody talks about when they show off military robotics: the same balance and actuator control that lets a machine carry heavy loads or absorb impact is exactly what’s being tested here — dynamic weight distribution, stance shifts, and controlled recovery. It may not look dramatic, but every small movement is training the system for stability under real-world conditions. Would you want something this size training next to real troops?
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THEY BUILT A COMBAT-GRADE EXOSKELETON. WATCH HOW IT MOVES. 🤖🩻 Dark green, “S-01” stamped across the chest plate, built with the kind of bulk you’d expect on a battlefield platform. This is a new exoskeleton prototype reportedly developed for the U.S. military, and in this clip it’s shifting its weight foot to foot, testing balance and stance in a parking lot outside what looks like an industrial facility. This is the part nobody talks about when they show off military robotics: the same balance and actuator control that lets a machine carry heavy loads or absorb impact is exactly what’s being tested here — dynamic weight distribution, stance shifts, and controlled recovery. It may not look dramatic, but every small movement is training the system for stability under real-world conditions. Would you want something this size training next to real troops?

DN_DEGEN

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Introducing Carbon AutoTractor! Today Carbon Robotics introduces the first and only tractor autonomy solution with real-time remote supervision and control. Powered by Carbon AI, Carbon AutoTractor is installed on existing tractors and is remotely monitored by Carbon Robotics operators, who handle interventions in real time to ensure seamless operation. Carbon AutoTractor delivers reliable, uninterrupted autonomous tractor operation–helping farmers increase productivity, reduce labor dependency, and scale operations. Developed with direct feedback from farmers, Carbon AutoTractor addresses the key barriers that have prevented the widespread adoption of tractor autonomy: frequent stoppages requiring farmer intervention, complex supervision and management, and unreliable solutions not built for 24/7 production operations. "Too often, autonomy in agriculture has been overpromised and underdelivered,” said Paul Mikesell, CEO and founder of Carbon Robotics. “We built Carbon AutoTractor to change that—by working closely with farmers every step of the way to design a solution rooted in simplicity, reliability, and the realities of modern farm operations. This is autonomy shaped by real-world experience, not just another tech experiment." Explore Carbon AutoTractor:
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Introducing Carbon AutoTractor! Today Carbon Robotics introduces the first and only tractor autonomy solution with real-time remote supervision and control. Powered by Carbon AI, Carbon AutoTractor is installed on existing tractors and is remotely monitored by Carbon Robotics operators, who handle interventions in real time to ensure seamless operation. Carbon AutoTractor delivers reliable, uninterrupted autonomous tractor operation–helping farmers increase productivity, reduce labor dependency, and scale operations. Developed with direct feedback from farmers, Carbon AutoTractor addresses the key barriers that have prevented the widespread adoption of tractor autonomy: frequent stoppages requiring farmer intervention, complex supervision and management, and unreliable solutions not built for 24/7 production operations. "Too often, autonomy in agriculture has been overpromised and underdelivered,” said Paul Mikesell, CEO and founder of Carbon Robotics. “We built Carbon AutoTractor to change that—by working closely with farmers every step of the way to design a solution rooted in simplicity, reliability, and the realities of modern farm operations. This is autonomy shaped by real-world experience, not just another tech experiment." Explore Carbon AutoTractor:

Carbon Robotics

340,386 Aufrufe • vor 1 Jahr

Ran 21 km (13.1 miles) — and the motor was still cold. That’s the detail that matters. 🤖 Honor was the clear dark horse in this year’s robot half marathon. They swept 1st, 2nd, and 3rd, and also posted a strong top-6 finish overall. What stands out to me is that this was not just about bigger motors, or a gait tuned for long-distance running. They seem to have solved something more important — cooling. In a post-race interview, Honor engineers said the robot used liquid-cooling tech adapted from Honor smartphones, with cooling lines running deep into the motor system to carry heat away. Some reports added more detail: the setup used two high-speed micro pumps, with flow rates reaching up to 6 liters per minute, giving the system enough cooling capacity to handle sustained lower-joint motor load. That matters because once a robot starts overheating, output drops, stability goes with it, and the whole run can fall apart fast. And that’s exactly why this detail is interesting. Of course, that does not mean Honor has already surpassed teams like TienKung or Unitree across humanoid robotics as a whole. What it does suggest is that for the marathon task, they built a very strong system solution. And honestly, that alone is already a useful case for the industry. The bigger trend is moving fast. Last year, TienKung won in around 2 hours 40 minutes. This year, the winning time dropped to 50 minutes 26 seconds. Last year, most robots were still fully remote-controlled or only semi-autonomous. This year, around 40% were running with a much higher level of autonomy. So to me, the real signal is not just that robots got faster. It’s that the field is now moving past raw speed, and into the harder problems: autonomy, stability, and system reliability under load. If the pace of progress stays anywhere close to this, then next year’s race should be even more worth watching.
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Ran 21 km (13.1 miles) — and the motor was still cold. That’s the detail that matters. 🤖 Honor was the clear dark horse in this year’s robot half marathon. They swept 1st, 2nd, and 3rd, and also posted a strong top-6 finish overall. What stands out to me is that this was not just about bigger motors, or a gait tuned for long-distance running. They seem to have solved something more important — cooling. In a post-race interview, Honor engineers said the robot used liquid-cooling tech adapted from Honor smartphones, with cooling lines running deep into the motor system to carry heat away. Some reports added more detail: the setup used two high-speed micro pumps, with flow rates reaching up to 6 liters per minute, giving the system enough cooling capacity to handle sustained lower-joint motor load. That matters because once a robot starts overheating, output drops, stability goes with it, and the whole run can fall apart fast. And that’s exactly why this detail is interesting. Of course, that does not mean Honor has already surpassed teams like TienKung or Unitree across humanoid robotics as a whole. What it does suggest is that for the marathon task, they built a very strong system solution. And honestly, that alone is already a useful case for the industry. The bigger trend is moving fast. Last year, TienKung won in around 2 hours 40 minutes. This year, the winning time dropped to 50 minutes 26 seconds. Last year, most robots were still fully remote-controlled or only semi-autonomous. This year, around 40% were running with a much higher level of autonomy. So to me, the real signal is not just that robots got faster. It’s that the field is now moving past raw speed, and into the harder problems: autonomy, stability, and system reliability under load. If the pace of progress stays anywhere close to this, then next year’s race should be even more worth watching.

RoboHub🤖

60,151 Aufrufe • vor 4 Monaten

Xynova just launched Flex 2, its second-gen hybrid dexterous hand — and Xiaomi is already on the cap table. 🤖✋ The spec sheet is sharp: 23 DOF, 400g palm, ±0.1mm repeatability, 0.05N force control, 12kg single-hand grasp load, and multimodal sensing for adaptive grasping and slip detection. Xynova is not only building a robotic hand. It is building the manipulation stack around it: micro linear actuators, joint modules, sensing, control, and developer-facing integration. The Xiaomi link is confirmed. Xiaomi Strategic Investment joined Xynova’s angel round and continued adding in the Pre-A round. What is not confirmed: whether Xiaomi’s own robot hand uses Xynova’s technology. That detail still has no public proof. But the direction is clear: Xiaomi is putting money into the component layer that decides whether humanoids can actually handle objects, tools, and daily tasks. This is the part of robotics that rarely looks flashy on stage. But it decides what a robot can really do with its hands.
0:35

Xynova just launched Flex 2, its second-gen hybrid dexterous hand — and Xiaomi is already on the cap table. 🤖✋ The spec sheet is sharp: 23 DOF, 400g palm, ±0.1mm repeatability, 0.05N force control, 12kg single-hand grasp load, and multimodal sensing for adaptive grasping and slip detection. Xynova is not only building a robotic hand. It is building the manipulation stack around it: micro linear actuators, joint modules, sensing, control, and developer-facing integration. The Xiaomi link is confirmed. Xiaomi Strategic Investment joined Xynova’s angel round and continued adding in the Pre-A round. What is not confirmed: whether Xiaomi’s own robot hand uses Xynova’s technology. That detail still has no public proof. But the direction is clear: Xiaomi is putting money into the component layer that decides whether humanoids can actually handle objects, tools, and daily tasks. This is the part of robotics that rarely looks flashy on stage. But it decides what a robot can really do with its hands.

RoboHub🤖

71,176 Aufrufe • vor 3 Monaten

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:
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Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 Aufrufe • vor 10 Monaten

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
1:05

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 Aufrufe • vor 9 Monaten

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.
2:50

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.

RoboHub🤖

209,946 Aufrufe • vor 4 Monaten

This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction
0:30

This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction

Techniahqrobot | humanoid robots

28,176 Aufrufe • vor 2 Monaten

Baby iguanas can run up to 20 mph to escape predators, like snakes. They are also very good at climbing, which gives them another way to escape predators. This stunning video shows the epic battle between a baby iguana and hundreds of hungry snakes! Their long, slender bodies and strong legs help them to move quickly. They can also change their color to blend in with their surroundings, making it more difficult for predators to see them. If a predator does catch up to them, baby iguanas can shed their tails as a distraction. The tail will grow back eventually, but it gives the iguana time to escape. Baby iguanas are fascinating creatures that have evolved to survive in their environment. #babyiguanas #speed #predators #snakes #fascinatingcreatures #Facts 📹 BBC Earth
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Baby iguanas can run up to 20 mph to escape predators, like snakes. They are also very good at climbing, which gives them another way to escape predators. This stunning video shows the epic battle between a baby iguana and hundreds of hungry snakes! Their long, slender bodies and strong legs help them to move quickly. They can also change their color to blend in with their surroundings, making it more difficult for predators to see them. If a predator does catch up to them, baby iguanas can shed their tails as a distraction. The tail will grow back eventually, but it gives the iguana time to escape. Baby iguanas are fascinating creatures that have evolved to survive in their environment. #babyiguanas #speed #predators #snakes #fascinatingcreatures #Facts 📹 BBC Earth

Facts

2,140,257 Aufrufe • vor 3 Jahren

Actress **Natalie Dormer** appearing on **The Late Late Show with Craig Ferguson**… and what starts as casual conversation quickly turns into playful late-night teasing when the topic shifts to her preference for older men. Craig immediately leans into the joke, turning the exchange into that signature back-and-forth the show became known for, where flirtation, sarcasm, and awkward humor all blend together naturally. Natalie handles it perfectly too, staying calm, witty, and completely comfortable matching the energy instead of pulling away from it. That’s what made these interviews stand out compared to more scripted talk shows, because the conversation never felt controlled, it just evolved in real time and became funnier the more unexpected it got. With someone like Natalie Dormer, who already carries that sharp and confident presence, the chemistry makes the whole moment feel effortless
0:34

Actress **Natalie Dormer** appearing on **The Late Late Show with Craig Ferguson**… and what starts as casual conversation quickly turns into playful late-night teasing when the topic shifts to her preference for older men. Craig immediately leans into the joke, turning the exchange into that signature back-and-forth the show became known for, where flirtation, sarcasm, and awkward humor all blend together naturally. Natalie handles it perfectly too, staying calm, witty, and completely comfortable matching the energy instead of pulling away from it. That’s what made these interviews stand out compared to more scripted talk shows, because the conversation never felt controlled, it just evolved in real time and became funnier the more unexpected it got. With someone like Natalie Dormer, who already carries that sharp and confident presence, the chemistry makes the whole moment feel effortless

Camila_

1,030,000 Aufrufe • vor 1 Monat

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.
0:31

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 Aufrufe • vor 7 Monaten

I have proof that the evolution of security can be fast – and cheaper than old defense systems. This is an iPad with software that lets us control our security in real time. It’s true. We have these iPads – I have one, my Prime Minister has one, our Minister of Defense, and our top military commanders – they have it. It lets us see the front line in Ukraine, and even every enemy killed – with video proof. Right now, 90% of Russian losses on the front are caused by our drones. That’s why it’s so important to know who has the advantage in drones – and to be fast and strong in defending against them. The iPad also shows every strike in our skies, our sea area, and our long-range strikes against Russia. It gives us real-time control over people’s safety, and our infrastructure and energy sector. And I believe the evolution of security will make it possible for every leader, every defense minister – and even ordinary people – to have tools like this, and with them, a high level of protection of life. Leaders and ministers work for people. And if the leaders have power, it is only because people trust them with their security. With tools like this, people will not only trust – they will be able to see for themselves what is being done for their security. In real time. From my address to the Parliament of the United Kingdom (3/7)
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I have proof that the evolution of security can be fast – and cheaper than old defense systems. This is an iPad with software that lets us control our security in real time. It’s true. We have these iPads – I have one, my Prime Minister has one, our Minister of Defense, and our top military commanders – they have it. It lets us see the front line in Ukraine, and even every enemy killed – with video proof. Right now, 90% of Russian losses on the front are caused by our drones. That’s why it’s so important to know who has the advantage in drones – and to be fast and strong in defending against them. The iPad also shows every strike in our skies, our sea area, and our long-range strikes against Russia. It gives us real-time control over people’s safety, and our infrastructure and energy sector. And I believe the evolution of security will make it possible for every leader, every defense minister – and even ordinary people – to have tools like this, and with them, a high level of protection of life. Leaders and ministers work for people. And if the leaders have power, it is only because people trust them with their security. With tools like this, people will not only trust – they will be able to see for themselves what is being done for their security. In real time. From my address to the Parliament of the United Kingdom (3/7)

Volodymyr Zelenskyy / Володимир Зеленський

522,722 Aufrufe • vor 5 Monaten

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:
0:54

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

21,319 Aufrufe • vor 5 Monaten