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A humanoid labeled Dragongwing IQ10 went down on stage and needed human intervention before the presentation could continue. That failure is useful. Balance recovery nnee torque floor contact restart behavior. These are the details that decide whether humanoids can work outside a booth. #HumanoidRobot #Robotics #PhysicalAI #AIRobotics

TechniaHQ | humanoid robots

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Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at hello@qualiastudios.dev More soon

Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at [email protected] More soon

Qualia

87,750 views • 2 months ago

The next manipulation tool may not live on your screen. It may stand in front of you look into your eyes and convince you that it understands. Would you rather face a humanoid robot strong enough to hurt you or one designed well enough to make you trust it? This robot copies blinking, eye contact, head movement and facial expressions to create the illusion of human presence. That may look impressive but it also opens a darker question. When a machine can look concerned appear friendly and imitate emotion people may start trusting signals that contain no real feeling no empathy and no moral responsibility. So which is more dangerous a robot with physical power or a robot that can manufacture trust? #HumanoidRobot #Robotics #AI

The next manipulation tool may not live on your screen. It may stand in front of you look into your eyes and convince you that it understands. Would you rather face a humanoid robot strong enough to hurt you or one designed well enough to make you trust it? This robot copies blinking, eye contact, head movement and facial expressions to create the illusion of human presence. That may look impressive but it also opens a darker question. When a machine can look concerned appear friendly and imitate emotion people may start trusting signals that contain no real feeling no empathy and no moral responsibility. So which is more dangerous a robot with physical power or a robot that can manufacture trust? #HumanoidRobot #Robotics #AI

Techniahqrobot | humanoid robots

11,650 views • 22 days ago

Qubic's original Useful Proof of Work (UPoW) was built on a powerful idea: that a decentralized network of small individual miners could contribute massive cumulative compute power to AIGarth — Qubic’s native AI product. This vision took shape before the global AI race truly began. Now, as major corporations increasingly rely on centralized data centers, the cracks are showing. Power grid bottlenecks, land grabs, and restrictive utility contracts, these are the limits Qubic was designed to bypass. None of that slows us down. Qubic’s UPoW isn’t just efficient. It’s future-proof by design. #QUBIC Qubic Proof of Useful Work:

Qubic's original Useful Proof of Work (UPoW) was built on a powerful idea: that a decentralized network of small individual miners could contribute massive cumulative compute power to AIGarth — Qubic’s native AI product. This vision took shape before the global AI race truly began. Now, as major corporations increasingly rely on centralized data centers, the cracks are showing. Power grid bottlenecks, land grabs, and restrictive utility contracts, these are the limits Qubic was designed to bypass. None of that slows us down. Qubic’s UPoW isn’t just efficient. It’s future-proof by design. #QUBIC Qubic Proof of Useful Work:

Qubic

16,029 views • 1 year ago

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

Miyu Horiuchi

90,718 views • 6 months ago

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

1,676,332 views • 8 months ago

The most dangerous part of a humanoid robot may be the face. UBTECH’s Walker shows how a machine can use gaze stillness and humanlike expression to create trust before proving judgment reliability or accountability. Should robots be allowed to look this human in public spaces? Putting the debate aside this is clearly a work of art. Congratulations to UBTECH for this achievement. Walker looks so refined that I may need to buy one fast before the stock runs out lol. Credit : UBTECH Robotics

The most dangerous part of a humanoid robot may be the face. UBTECH’s Walker shows how a machine can use gaze stillness and humanlike expression to create trust before proving judgment reliability or accountability. Should robots be allowed to look this human in public spaces? Putting the debate aside this is clearly a work of art. Congratulations to UBTECH for this achievement. Walker looks so refined that I may need to buy one fast before the stock runs out lol. Credit : UBTECH Robotics

Techniahqrobot | humanoid robots

51,556 views • 1 month ago

It's the little things that make me excited about the future. Many people are (rightfully) worried that AI and robotics will take their jobs. Yes, it's about money they need to live on, and solutions are needed. At the same time, it's often work that's strenuous and tedious. So we can be glad when robots take these tasks off our hands. Furthermore, robots don't need breaks, vacations, or sleep. They can be scaled quickly and are efficient. Globalization, and humanity as a whole, can therefore benefit massively from this increase in efficiency.

It's the little things that make me excited about the future. Many people are (rightfully) worried that AI and robotics will take their jobs. Yes, it's about money they need to live on, and solutions are needed. At the same time, it's often work that's strenuous and tedious. So we can be glad when robots take these tasks off our hands. Furthermore, robots don't need breaks, vacations, or sleep. They can be scaled quickly and are efficient. Globalization, and humanity as a whole, can therefore benefit massively from this increase in efficiency.

Chubby♨️

25,528 views • 4 months ago

Figure 03 just finished an 8-hour work livestream, imperfect, but already good enough to replace a lot of repetitive warehouse labor. 🤖 Brett Adcock put a team of F.03 robots on a factory-style package sorting task for a full shift. The job was simple and brutal: detect the barcode, pick the package, flip it label-side down, place it on the conveyor, repeat. Soft poly bags, rigid boxes, moving belts, messy orientations. That is exactly the kind of boring physical work factories pay humans to do all day. Early in the stream, the system handled 230 packages in 10 minutes. That is roughly 2.6 seconds per item — already in human-speed territory for this narrow workflow. The more important part: it was not one robot pretending to work all day. It was a team of Figure 03 robots keeping the line running. When one robot ran low on battery, it left the station and another robot stepped in. That is the real factory signal: not just autonomy, but shift continuity. F.03 is rated for about 5 hours of runtime, so the 8-hour result depends on fleet orchestration, charging, and handoff. That matters more than a single clean demo. The stream was not perfect. There were pauses, hesitations, missed orientations, and small recovery moments. Good. A perfect short clip hides failure. An 8-hour livestream exposes the parts that actually matter: endurance, recovery, throughput, and whether the robot can stay useful after the novelty wears off. Figure says this was fully autonomous on Helix-02, with zero human intervention. For logistics and manufacturing, that is the threshold worth watching. Not “can it do one impressive task?” Can it keep doing the boring task for an entire shift? Figure is not showing a general human replacement yet. But for structured, repetitive factory work, the gap just got much smaller. The timing is also interesting: Figure says BotQ has already delivered 350+ F.03 units and reached a 1 robot/hour production cadence. And F.04 is now in full design lock, with parts starting to ship. The next test is obvious. 8 hours was the proof of endurance. 24/7 is the proof of labor economics.

Figure 03 just finished an 8-hour work livestream, imperfect, but already good enough to replace a lot of repetitive warehouse labor. 🤖 Brett Adcock put a team of F.03 robots on a factory-style package sorting task for a full shift. The job was simple and brutal: detect the barcode, pick the package, flip it label-side down, place it on the conveyor, repeat. Soft poly bags, rigid boxes, moving belts, messy orientations. That is exactly the kind of boring physical work factories pay humans to do all day. Early in the stream, the system handled 230 packages in 10 minutes. That is roughly 2.6 seconds per item — already in human-speed territory for this narrow workflow. The more important part: it was not one robot pretending to work all day. It was a team of Figure 03 robots keeping the line running. When one robot ran low on battery, it left the station and another robot stepped in. That is the real factory signal: not just autonomy, but shift continuity. F.03 is rated for about 5 hours of runtime, so the 8-hour result depends on fleet orchestration, charging, and handoff. That matters more than a single clean demo. The stream was not perfect. There were pauses, hesitations, missed orientations, and small recovery moments. Good. A perfect short clip hides failure. An 8-hour livestream exposes the parts that actually matter: endurance, recovery, throughput, and whether the robot can stay useful after the novelty wears off. Figure says this was fully autonomous on Helix-02, with zero human intervention. For logistics and manufacturing, that is the threshold worth watching. Not “can it do one impressive task?” Can it keep doing the boring task for an entire shift? Figure is not showing a general human replacement yet. But for structured, repetitive factory work, the gap just got much smaller. The timing is also interesting: Figure says BotQ has already delivered 350+ F.03 units and reached a 1 robot/hour production cadence. And F.04 is now in full design lock, with parts starting to ship. The next test is obvious. 8 hours was the proof of endurance. 24/7 is the proof of labor economics.

RoboHub🤖

16,818 views • 3 months ago

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

Oier Mees

12,277 views • 1 month ago

CES was an enormous success. We saw thousands of people and a few dogs interacting with our agents and robots. We booked this booth at the last minute, mainly to book next year’s booth. Truly extraordinary what our team was able to put together on such short notice. Next year we reserved one of the largest booths at CES in the highest traffic location for AI and Robotics. We plan to unveil something extraordinary. Until the next show, we will be debuting the first custom models that will emerge from the first humanoid robotics hot rod shop. These models will be walking around, talking and interacting at tradeshows all over the world over the next few months. We are in the process of acquiring every humanoid robot available on the market to modify these as well. Stay tuned for more updates, the future is here and it’s rapidly accelerating.

CES was an enormous success. We saw thousands of people and a few dogs interacting with our agents and robots. We booked this booth at the last minute, mainly to book next year’s booth. Truly extraordinary what our team was able to put together on such short notice. Next year we reserved one of the largest booths at CES in the highest traffic location for AI and Robotics. We plan to unveil something extraordinary. Until the next show, we will be debuting the first custom models that will emerge from the first humanoid robotics hot rod shop. These models will be walking around, talking and interacting at tradeshows all over the world over the next few months. We are in the process of acquiring every humanoid robot available on the market to modify these as well. Stay tuned for more updates, the future is here and it’s rapidly accelerating.

Parallel

16,873 views • 1 year ago

Trump has agreed to debate Harris on September 4th on Fox. There was no mention of RFK Jr. It’s critical whether you’re a Republican, Democrat, RFK, or other third party voter, that we advocate for RFK to be on that stage. A heavy majority of Americans agree that RFK should have a spot in the debate. 7/10 Americans wanted him on the stage during the last debate, that number is no doubt equal or larger this debate. After the disastrous first debate with Trump/Biden on CNN, it is hard to argue that if RFK were on the stage that day that he could have had a chance to be polling at levels near Trump/Harris currently. It does not mean that he would be, but 100% a rational conclusion that he could be. Americans need someone on the stage to ask difficult questions that the moderators are unwilling to bring up because of the unwritten rules of hosting presidential debates. If it is a debate only between Trump/Harris, Trump will spew his talking points about Harris’s disastrous past in politics, and Harris will spew her talking points of Trump being a racist and threat to Democracy. In reality Americans don’t want to hear these talking points anymore, we already get it at nausea everyday through social media. What America truly deserves is a presidential debate where Trump, Harris, RFK, Stein, West, and Oliver are on the stage but it’s a sad truth that at first we just need to fight to try and get a third candidate on the stage before 6.

Trump has agreed to debate Harris on September 4th on Fox. There was no mention of RFK Jr. It’s critical whether you’re a Republican, Democrat, RFK, or other third party voter, that we advocate for RFK to be on that stage. A heavy majority of Americans agree that RFK should have a spot in the debate. 7/10 Americans wanted him on the stage during the last debate, that number is no doubt equal or larger this debate. After the disastrous first debate with Trump/Biden on CNN, it is hard to argue that if RFK were on the stage that day that he could have had a chance to be polling at levels near Trump/Harris currently. It does not mean that he would be, but 100% a rational conclusion that he could be. Americans need someone on the stage to ask difficult questions that the moderators are unwilling to bring up because of the unwritten rules of hosting presidential debates. If it is a debate only between Trump/Harris, Trump will spew his talking points about Harris’s disastrous past in politics, and Harris will spew her talking points of Trump being a racist and threat to Democracy. In reality Americans don’t want to hear these talking points anymore, we already get it at nausea everyday through social media. What America truly deserves is a presidential debate where Trump, Harris, RFK, Stein, West, and Oliver are on the stage but it’s a sad truth that at first we just need to fight to try and get a third candidate on the stage before 6.

End Tribalism in Politics

60,444 views • 2 years ago

I'm seeing a lot of people saying it is just a bad upscaling instead of Gen AI, but it is indeed Gen AI animation. There are things going on in it that just don't make any sense for puppet animation. Here are some parts that make it clear, I slowed down the video so it's easier to notice the chunky parts. I checked the past ADs and it's possible to notice details (from an animator's POV) that are common and make sense for human work, so it was definitely a choice to use AI on this new AD

I'm seeing a lot of people saying it is just a bad upscaling instead of Gen AI, but it is indeed Gen AI animation. There are things going on in it that just don't make any sense for puppet animation. Here are some parts that make it clear, I slowed down the video so it's easier to notice the chunky parts. I checked the past ADs and it's possible to notice details (from an animator's POV) that are common and make sense for human work, so it was definitely a choice to use AI on this new AD

mei ✨ live2d animator

1,114,561 views • 7 months ago

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. Why this matters if you want robotics to deliver real value on your floor: • Handles mixed glass with random poses and no fixed fixtures. • Runs real grasp selection under noise, vibration and production variability. • Maintains throughput while avoiding breakage on a delicate material. • Shows mobile manipulation doing actual shift work instead of controlled lab runs. Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. The direction is clear. Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success. —- Weekly robotics and AI insights. Subscribe free:

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. Why this matters if you want robotics to deliver real value on your floor: • Handles mixed glass with random poses and no fixed fixtures. • Runs real grasp selection under noise, vibration and production variability. • Maintains throughput while avoiding breakage on a delicate material. • Shows mobile manipulation doing actual shift work instead of controlled lab runs. Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. The direction is clear. Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

24,743 views • 8 months ago

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 views • 5 months ago

E pluribus unum – “out of many, one” – is not only a motto for the United States. It’s a good credo for microrobots. A research collaboration between Cornell and the Max Planck Institute for Intelligent Systems has shown how a swarm of microrobots spinning on a water surface can together generate the fluidic torque needed to manipulate passive structures without any physical contact. This collective behavior was demonstrated to operate gears and move objects, with the aim of eventually performing microscale tasks and biomedical procedures. “At small scales, contact-based manipulation can be limiting, and flow-based manipulation offers a great alternative,” said Kirstin Petersen, associate professor and an Aref and Manon Lahham Faculty Fellow in the Department of Electrical and Computer Engineering in Cornell Duffield College of Engineering and the paper’s co-senior author. “We’re showing that in spite of their size, adding more microrobots creates stronger flows and greater torque transfer.” The research published Feb. 25 in Science Advances. The lead author is Steven Ceron, Ph.D. ’22, now an assistant professor at the University of Michigan. More:

E pluribus unum – “out of many, one” – is not only a motto for the United States. It’s a good credo for microrobots. A research collaboration between Cornell and the Max Planck Institute for Intelligent Systems has shown how a swarm of microrobots spinning on a water surface can together generate the fluidic torque needed to manipulate passive structures without any physical contact. This collective behavior was demonstrated to operate gears and move objects, with the aim of eventually performing microscale tasks and biomedical procedures. “At small scales, contact-based manipulation can be limiting, and flow-based manipulation offers a great alternative,” said Kirstin Petersen, associate professor and an Aref and Manon Lahham Faculty Fellow in the Department of Electrical and Computer Engineering in Cornell Duffield College of Engineering and the paper’s co-senior author. “We’re showing that in spite of their size, adding more microrobots creates stronger flows and greater torque transfer.” The research published Feb. 25 in Science Advances. The lead author is Steven Ceron, Ph.D. ’22, now an assistant professor at the University of Michigan. More:

Cornell University

52,343 views • 5 months ago

There's something very funny about Optimus explaining first principles. A humanoid robot calmly describing the mindset that helped create rockets, EVs, factories, and eventually itself. First principles thinking is basically Elon refusing to accept the “because that’s how it’s always done” answer. At Tesla and SpaceX, the question is not only how to make the old thing better. It is whether the old thing should exist in that form at all. Why throw away a rocket booster if the physics says it can come back? Why build an EV like a compliance car if batteries, software, motors, and factories can be redesigned around performance and scale? They strip the problem down to physics, materials, cost, and function, then rebuild upward from what is actually true. Very useful for building things that did not exist before. Tesla Optimus Tesla SpaceX Elon Musk / Writer: Annette, Designer: Janné

There's something very funny about Optimus explaining first principles. A humanoid robot calmly describing the mindset that helped create rockets, EVs, factories, and eventually itself. First principles thinking is basically Elon refusing to accept the “because that’s how it’s always done” answer. At Tesla and SpaceX, the question is not only how to make the old thing better. It is whether the old thing should exist in that form at all. Why throw away a rocket booster if the physics says it can come back? Why build an EV like a compliance car if batteries, software, motors, and factories can be redesigned around performance and scale? They strip the problem down to physics, materials, cost, and function, then rebuild upward from what is actually true. Very useful for building things that did not exist before. Tesla Optimus Tesla SpaceX Elon Musk / Writer: Annette, Designer: Janné

Mario Nawfal

65,990 views • 24 days ago

The train is spotlessly clean, yet this man proudly displays zero civic sense 🤡 by throwing peanut shells on the floor and turning the coach into a dustbin. This is the exact mentality that keeps India struggling. The Railways clean the trains again and again and people like these destroy it within minutes. What else can the Railways possibly do? For such behavior, strict enforcement and heavy fines are the only solutions. Public places are not your personal garbage bin. Keep them clean the way you keep your own home clean. 🛑 Clean India is impossible until mindsets are cleaned first.

The train is spotlessly clean, yet this man proudly displays zero civic sense 🤡 by throwing peanut shells on the floor and turning the coach into a dustbin. This is the exact mentality that keeps India struggling. The Railways clean the trains again and again and people like these destroy it within minutes. What else can the Railways possibly do? For such behavior, strict enforcement and heavy fines are the only solutions. Public places are not your personal garbage bin. Keep them clean the way you keep your own home clean. 🛑 Clean India is impossible until mindsets are cleaned first.

The Nalanda Index

107,364 views • 6 months ago

Anni is the founder of bottom-up policymaking in Maldives. Before democracy, everything was decided by the president and handed down to the people from the top. But Anni changed that. He taught the people that they themselves should decide their own future. This is the kind of leader he is. A few days after he and Faya shared new ideas at the Bodu Badhalu press conference, Anni and Faya are out meeting ordinary people. He sits with them, listens carefully to their thoughts and opinions on those ideas, and writes everything down. In this video, you can see an elderly man suggesting a special assembly for big changes and Anni notes it down too. Anni truly believes the people should have a voice in shaping the country’s future. That is what makes him a great leader. #RestoreMDP

Anni is the founder of bottom-up policymaking in Maldives. Before democracy, everything was decided by the president and handed down to the people from the top. But Anni changed that. He taught the people that they themselves should decide their own future. This is the kind of leader he is. A few days after he and Faya shared new ideas at the Bodu Badhalu press conference, Anni and Faya are out meeting ordinary people. He sits with them, listens carefully to their thoughts and opinions on those ideas, and writes everything down. In this video, you can see an elderly man suggesting a special assembly for big changes and Anni notes it down too. Anni truly believes the people should have a voice in shaping the country’s future. That is what makes him a great leader. #RestoreMDP

Hassan Kurusee

53,293 views • 3 months ago

ENGINEAI just opened registration for URKL, a global humanoid fighting league with an insane ¥10,000,000 (approx. $1.39 million) top prize. 🤖🥊 This is a massive engineering challenge focused on motion control and balance using the "T800" humanoid as the standard bot. The rules are strictly "non-violent," meaning no destructive mods are allowed. You win through better code and smarter protective gear. Here is the breakdown for teams looking to jump in: ➤ Massive Payouts: The winner takes ¥10,000,000 (approx. $1.39 million), second gets ¥2,000,000 (approx. $278,000), and third takes ¥1,000,000 (approx. $139,000). ➤ Hardware Perks: Every team that makes it into the Top 16 officially owns their T800 robot. ➤ Career Fast-Track: Top 8 finalists get a "Green Channel" straight to the final interview for job offers at ENGINEAI. ➤ Registration: Open from March 1 to April 30. Teams need at least 3 members with skills in control, electronics, or mechanical design. ➤ Global Finals: After the qualifiers, the world championship is set for December 2026 through January 2027. Once you are in, the committee hands over the simulation platform and T800 models to start training your boxing algorithms. Full Info: #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #URKL #ENGINEAI #RobotFighting

ENGINEAI just opened registration for URKL, a global humanoid fighting league with an insane ¥10,000,000 (approx. $1.39 million) top prize. 🤖🥊 This is a massive engineering challenge focused on motion control and balance using the "T800" humanoid as the standard bot. The rules are strictly "non-violent," meaning no destructive mods are allowed. You win through better code and smarter protective gear. Here is the breakdown for teams looking to jump in: ➤ Massive Payouts: The winner takes ¥10,000,000 (approx. $1.39 million), second gets ¥2,000,000 (approx. $278,000), and third takes ¥1,000,000 (approx. $139,000). ➤ Hardware Perks: Every team that makes it into the Top 16 officially owns their T800 robot. ➤ Career Fast-Track: Top 8 finalists get a "Green Channel" straight to the final interview for job offers at ENGINEAI. ➤ Registration: Open from March 1 to April 30. Teams need at least 3 members with skills in control, electronics, or mechanical design. ➤ Global Finals: After the qualifiers, the world championship is set for December 2026 through January 2027. Once you are in, the committee hands over the simulation platform and T800 models to start training your boxing algorithms. Full Info: #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #URKL #ENGINEAI #RobotFighting

RoboHub🤖

30,637 views • 5 months ago

Chinese factories can build a car in 77 seconds, with robots doing the work at a scale the U.S. cannot match right now. And Trump is out here talking about tariffs like they are a magic switch that makes America “make things again.” Look at the automation in that kind of plant. Even with tariffs, it would take the U.S. decades, realistically 40 years, to rebuild the industrial base and the machine building capability needed to produce that level of robotics at scale, then stack enough volume to get true economies of scale. And for what, exactly. U.S. cars barely have a market outside the U.S. Meanwhile China builds for the world. As for the rest of the planet watching America’s tariff daydreams: couldn’t the world care less.

Chinese factories can build a car in 77 seconds, with robots doing the work at a scale the U.S. cannot match right now. And Trump is out here talking about tariffs like they are a magic switch that makes America “make things again.” Look at the automation in that kind of plant. Even with tariffs, it would take the U.S. decades, realistically 40 years, to rebuild the industrial base and the machine building capability needed to produce that level of robotics at scale, then stack enough volume to get true economies of scale. And for what, exactly. U.S. cars barely have a market outside the U.S. Meanwhile China builds for the world. As for the rest of the planet watching America’s tariff daydreams: couldn’t the world care less.

Gandalv

360,670 views • 5 months ago