正在加载视频...

视频加载失败

Midea has just introduced a six-armed super-humanoid robot designed for complex task handling and multi-step operations,essentially functioning as a self-contained workstation. The new humanoid is an upgraded version of the earlier Miro wheeled humanoid robot. In August, Miro was deployed at Midea’s Jingzhou factory in Hubei, working alongside AMRs,...

31,833 次观看 • 7 个月前 •via X (Twitter)

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

BOOM! Humanoid Robots Just Performed Surgery for the First Time! REAL VIDEO! In a groundbreaking preclinical breakthrough, researchers at UC San Diego have achieved what many thought was years away: teleoperated humanoid robots successfully completing live surgeries. Published in Nature, the study marks the world’s first use of humanoid robots for in-vivo laparoscopic procedures on large animals (pigs). Two separate surgeries were completed: Key Details •. Procedure: Laparoscopic gallbladder removal (cholecystectomy) •. Team 1: Human surgeon + one humanoid robot (the robot performed core tasks while the human assisted) •. Team 2: Two humanoid robots working together with no human at the operating table •. Robots: Custom “Surgie” humanoids (~5 ft tall, ~60 lbs) using standard surgical tools •. Control: Fully teleoperated by surgeons (remote human control, not autonomous) •. Significance: First demonstration of humanoid robots handling real surgical workflows in a live setting, proving compatibility with existing OR tools and spaces This proof shows humanoid robots could one day help address surgeon shortages, enable remote procedures in rural areas, battlefields, or even space all at a fraction of the cost and space of traditional surgical robots like da Vinci. Read the full publication here: Project page with video: The future of surgery just got a whole lot more interesting. And medical cost for the first time in decades will be scheduled to go down, much further down.

Brian Roemmele

107,357 次观看 • 19 天前

For the first time in the history of automation, the people most likely to be replaced are the ones building their own replacement, frame by frame, for a few dollars an hour. Across India, Nigeria, China, and Argentina, workers are strapping cameras to their heads and recording every fold of laundry, every stitch, every washed dish, and that footage is training the robots designed to do those exact jobs. This is documented, not rumor. No jokes! Garment workers in Tamil Nadu, India have been filmed wearing head-mounted cameras on the factory floor, sending point-of-view footage to data firms whose clients include Fortune 500 companies. One US company alone has hired thousands of workers across more than 50 countries to record themselves cooking, cleaning, and folding clothes. More than 6 billion dollars poured into humanoid robots last year, and the one ingredient every maker is starved for is precisely this: real human hands doing real human work. The endpoint is stated plainly by the buyers. In China, one supplier said his pitch to factories is to let workers wear the cameras now, because trained robots will eventually work there instead. The quiet part is the exchange itself. The worker is paid for the hour and keeps nothing after it. No share, no royalty, no ownership of the movements their own body is teaching the machine. The skill leaves their hands and becomes someone else's product, and almost no one along the chain sees the full shape of the trade, not always the person filming, not the millions who watch the clip and scroll on. One scene holds all of it. A humanoid robot spent an hour folding three shirts while a human housekeeper, hired to guide it, quietly finished the rest of the chores. Every automation before this arrived from the outside. A machine showed up and took the job. This one is being built from the inside, by the workers themselves, handing over the last thing they had left to sell. UBI ? or something totally else should pave the way in the future? Thoughts?

Shanaka Anslem Perera ⚡

94,579 次观看 • 1 个月前

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 次观看 • 2 个月前

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 次观看 • 1 年前

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 次观看 • 5 个月前