Загрузка видео...

Не удалось загрузить видео

На главную

⚠️ NO MAGIC INVOLVED. JUST THE SAME ACTUATORS FROM HUMANOID ROBOTS, POINTED IN A DIFFERENT DIRECTION. What looks like sci-fi on an auto show floor runs on the same three technologies now putting robots on their feet. → Electric actuators instead of hydraulics, the same components going into today's...

Shred_0x's profile picture

Shredder

4,029 subscribers

132,402 просмотров • 9 дней назад •via X (Twitter)

ИскусствоНаука и технологииОбразование
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

⚠️ THE WEIRDEST PART OF THIS ROBOT DEMO? NOBODY SEEMS SURPRISED ANYMORE! Humanoid robots are moving through a crowded public space while people walk around them like they're just another part of the environment. There’s no empty laboratory, no isolated testing area and no perfect path marked on the floor — just machines and humans sharing the same space. That makes this feel less like a robot demonstration and more like a preview of everyday life. Getting one humanoid to walk is one problem. Getting several machines to move safely around unpredictable people is another, because humans constantly stop, turn, cross paths and change direction without warning. Every movement requires the robots to perceive what is happening around them, maintain balance and continuously adjust their path. That's the shift worth paying attention to. The milestone isn't simply that robots can walk among humans — it's that we're starting to see environments where they can actually belong. Once that becomes reliable enough for shopping centers, airports, factories and offices, the question stops being whether humanoids can enter our world. It becomes what we're going to ask them to do once they're here.
0:12

⚠️ THE WEIRDEST PART OF THIS ROBOT DEMO? NOBODY SEEMS SURPRISED ANYMORE! Humanoid robots are moving through a crowded public space while people walk around them like they're just another part of the environment. There’s no empty laboratory, no isolated testing area and no perfect path marked on the floor — just machines and humans sharing the same space. That makes this feel less like a robot demonstration and more like a preview of everyday life. Getting one humanoid to walk is one problem. Getting several machines to move safely around unpredictable people is another, because humans constantly stop, turn, cross paths and change direction without warning. Every movement requires the robots to perceive what is happening around them, maintain balance and continuously adjust their path. That's the shift worth paying attention to. The milestone isn't simply that robots can walk among humans — it's that we're starting to see environments where they can actually belong. Once that becomes reliable enough for shopping centers, airports, factories and offices, the question stops being whether humanoids can enter our world. It becomes what we're going to ask them to do once they're here.

Future Memo

31,170 просмотров • 14 дней назад

🇨🇳 CHINA JUST ROLLED OUT A NATIONAL ID SYSTEM FOR HUMANOID ROBOTS. They built the global standard. Every robot now carries a 29-digit ID. Modeled on China's citizen ID, but with cross-border tracking baked into the first 2 digits. This isn't surveillance. It's a passport system. The play: set the framework domestically, scale the install base, export the protocol as the global default. Same playbook that made Huawei the backbone of 5G. While the West argues about whether AI needs rules, China is already writing them.
0:17

🇨🇳 CHINA JUST ROLLED OUT A NATIONAL ID SYSTEM FOR HUMANOID ROBOTS. They built the global standard. Every robot now carries a 29-digit ID. Modeled on China's citizen ID, but with cross-border tracking baked into the first 2 digits. This isn't surveillance. It's a passport system. The play: set the framework domestically, scale the install base, export the protocol as the global default. Same playbook that made Huawei the backbone of 5G. While the West argues about whether AI needs rules, China is already writing them.

CryptoGoos

30,809 просмотров • 2 месяцев назад

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.
0:12

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

107,600 просмотров • 1 месяц назад

China now has its own “Bolt” — a robot named after sprint legend Usain Bolt. A Chinese research team has unveiled the world’s first full-size humanoid robot to reach a peak speed of 10 meters per second, setting a new global benchmark for humanoid running. Bolt runs like a body pushed to the limit. Its joints and power systems work in tight coordination, keeping it balanced even at sprint speed. Built to match the build of an adult man—1.75 meters tall and 75 kilograms—it is a life-sized system operating at the edge of physics. Compared with Usain Bolt’s iconic 9.58-second 100-meter world record, which many experts believe may stand for decades, the gap between humans and machines is narrowing fast. Chinese robots are now challenging the ceiling of human performance—much as AlphaGo once challenged Go champion Ke Jie. The breakthrough builds on earlier world-record achievements in high-speed robotic running and marks a giant leap for China in humanoid motion and control. Beyond records, Bolt also carries practical value: robots are leaving the lab and stepping into real-world settings—sports training, emergency response, and demanding industrial tasks where speed, balance and control truly matter.
0:10

China now has its own “Bolt” — a robot named after sprint legend Usain Bolt. A Chinese research team has unveiled the world’s first full-size humanoid robot to reach a peak speed of 10 meters per second, setting a new global benchmark for humanoid running. Bolt runs like a body pushed to the limit. Its joints and power systems work in tight coordination, keeping it balanced even at sprint speed. Built to match the build of an adult man—1.75 meters tall and 75 kilograms—it is a life-sized system operating at the edge of physics. Compared with Usain Bolt’s iconic 9.58-second 100-meter world record, which many experts believe may stand for decades, the gap between humans and machines is narrowing fast. Chinese robots are now challenging the ceiling of human performance—much as AlphaGo once challenged Go champion Ke Jie. The breakthrough builds on earlier world-record achievements in high-speed robotic running and marks a giant leap for China in humanoid motion and control. Beyond records, Bolt also carries practical value: robots are leaving the lab and stepping into real-world settings—sports training, emergency response, and demanding industrial tasks where speed, balance and control truly matter.

Sinical

111,438 просмотров • 6 месяцев назад

Sean Elliot thinks Butler got away with an extra step. Let's examine the play. Remember: the foot on the floor when a progressing player catches a pass is the gather step and doesn't count towards the legal two steps from that point-which is all Butler does. It makes no difference that both feet were off the floor in between step 1 and step 2. It's still just a step in the rulebook. Legal, as the naked eye can't discern any gap between control of ball and gather step down
0:15

Sean Elliot thinks Butler got away with an extra step. Let's examine the play. Remember: the foot on the floor when a progressing player catches a pass is the gather step and doesn't count towards the legal two steps from that point-which is all Butler does. It makes no difference that both feet were off the floor in between step 1 and step 2. It's still just a step in the rulebook. Legal, as the naked eye can't discern any gap between control of ball and gather step down

BBALLBREAKDOWN

60,809 просмотров • 9 месяцев назад

Model S and Model X just created room for Tesla's next obsession. And what gets the floor space? Optimus. Think about that for a second. The company that built its name on cars is now prepping production lines capable of building millions of humanoid robots. When Tesla starts rearranging factories around a product, pay attention. Optimus at scale is coming. Clear the way. Tesla
0:16

Model S and Model X just created room for Tesla's next obsession. And what gets the floor space? Optimus. Think about that for a second. The company that built its name on cars is now prepping production lines capable of building millions of humanoid robots. When Tesla starts rearranging factories around a product, pay attention. Optimus at scale is coming. Clear the way. Tesla

Mario Nawfal

72,755 просмотров • 2 месяцев назад

WATCH THE TONGUE, NOT THE SKIN 2026 AI Robot Exhibition floor. Guy peels protective wrap off a female humanoid - realistic skin, styled hair, standing still. He touches her chin. Her mouth opens. Her tongue extends, slightly. He turns to the camera and tells you the skin texture is indistinguishable from human. He's pointing at the wrong part. - Every previous humanoid demo hides the mouth Closed lips. Controlled smiles. Interview shots that cut before the mouth has to do anything complicated. There's a reason. Mouth interior is where the uncanny valley kills the illusion faster than anywhere else on a humanoid - wet surfaces, muscle deformation, teeth showing at wrong angles, a tongue that reads as rubber. The safe move for every manufacturer up to now has been to keep it shut. - Extending a tongue means the interior is solved What this demo is actually flexing: jaw and tongue actuators independent of the smile mechanism. A soft-tissue tongue with texture that reads correctly at close range. Cheek deformation responding to his chin-touch as a tactile input - that's a sensor layer under the silicone, not just a scripted animation. This is three separate hardware systems working together on a live floor with cameras inches away. - Skin texture is a commodity now. Mouth mechanics aren't. Every manufacturer shipping humanoids in 2026 has convincing skin - platinum-cure silicone with subcutaneous tinting is a known process at this point. That's not the differentiator anymore. The differentiator is what happens when you touch the unit. The one that opens its mouth on demand, on a public floor, in front of cameras, is the one that isn't hiding from close inspection. - Why the trade show matters The 2026 AI Robot Exhibition is China's biggest humanoid showcase - where enterprise buyers from hospitality, retail, healthcare and elder-care come to sign purchase orders. Willingness to demo interior mouth mechanics in that room is a confidence signal directly aimed at those buyers. "Our unit can talk to your customers with visible tongue and teeth motion and not look wrong doing it." - What this actually unlocks Humanoid receptionists that hold a conversation without the customer noticing something's off around the mouth. Retail units that can smile with teeth showing. Healthcare aides that speak to patients at bedside range. The close-inspection barrier - the two-foot distance where the illusion has always broken - just dropped a floor. Which is the distance almost every commercial humanoid deployment actually operates at.
0:10

WATCH THE TONGUE, NOT THE SKIN 2026 AI Robot Exhibition floor. Guy peels protective wrap off a female humanoid - realistic skin, styled hair, standing still. He touches her chin. Her mouth opens. Her tongue extends, slightly. He turns to the camera and tells you the skin texture is indistinguishable from human. He's pointing at the wrong part. - Every previous humanoid demo hides the mouth Closed lips. Controlled smiles. Interview shots that cut before the mouth has to do anything complicated. There's a reason. Mouth interior is where the uncanny valley kills the illusion faster than anywhere else on a humanoid - wet surfaces, muscle deformation, teeth showing at wrong angles, a tongue that reads as rubber. The safe move for every manufacturer up to now has been to keep it shut. - Extending a tongue means the interior is solved What this demo is actually flexing: jaw and tongue actuators independent of the smile mechanism. A soft-tissue tongue with texture that reads correctly at close range. Cheek deformation responding to his chin-touch as a tactile input - that's a sensor layer under the silicone, not just a scripted animation. This is three separate hardware systems working together on a live floor with cameras inches away. - Skin texture is a commodity now. Mouth mechanics aren't. Every manufacturer shipping humanoids in 2026 has convincing skin - platinum-cure silicone with subcutaneous tinting is a known process at this point. That's not the differentiator anymore. The differentiator is what happens when you touch the unit. The one that opens its mouth on demand, on a public floor, in front of cameras, is the one that isn't hiding from close inspection. - Why the trade show matters The 2026 AI Robot Exhibition is China's biggest humanoid showcase - where enterprise buyers from hospitality, retail, healthcare and elder-care come to sign purchase orders. Willingness to demo interior mouth mechanics in that room is a confidence signal directly aimed at those buyers. "Our unit can talk to your customers with visible tongue and teeth motion and not look wrong doing it." - What this actually unlocks Humanoid receptionists that hold a conversation without the customer noticing something's off around the mouth. Retail units that can smile with teeth showing. Healthcare aides that speak to patients at bedside range. The close-inspection barrier - the two-foot distance where the illusion has always broken - just dropped a floor. Which is the distance almost every commercial humanoid deployment actually operates at.

capONE 💎

15,360 просмотров • 6 дней назад

⚠️ EVERY PROFESSION BUILT ON TRUST FIRST BUILT AN APPEARANCE OF AUTHORITY. Doctors wear white coats not because it helps them diagnose. Studies confirmed it makes patients comply faster. Pilots wear stripes because passengers don't follow safety instructions from someone who looks like they wandered in off the street. Appearance of authority precedes actual authority. That pattern is old and documented. China's hyper-realistic humanoid face is the same playbook applied to a machine. A robot with servo joints and camera eyes gets questioned at the point of contact. A robot that holds eye contact and mirrors your micro-expressions gets deferred to. The uncanny valley was never the problem they were solving. It was the moat they were crossing.
0:17

⚠️ EVERY PROFESSION BUILT ON TRUST FIRST BUILT AN APPEARANCE OF AUTHORITY. Doctors wear white coats not because it helps them diagnose. Studies confirmed it makes patients comply faster. Pilots wear stripes because passengers don't follow safety instructions from someone who looks like they wandered in off the street. Appearance of authority precedes actual authority. That pattern is old and documented. China's hyper-realistic humanoid face is the same playbook applied to a machine. A robot with servo joints and camera eyes gets questioned at the point of contact. A robot that holds eye contact and mirrors your micro-expressions gets deferred to. The uncanny valley was never the problem they were solving. It was the moat they were crossing.

Shredder

63,002 просмотров • 20 дней назад

🚨 BREAKING: Physics just broke its own rulebook. For decades, every particle in the universe fit into 2 types • Bosons (share space) • Fermions (avoid each other) Now? Scientists just created something in between. A new class of particles called anyons that can behave partly like both. Even crazier In 1D systems, you can tune their behavior like a dial between order and exclusion. This means Particle identity isn’t fixed Quantum behavior is adjustable Reality depends on dimensional constraints Think about that. If the rules change based on dimension. then what we call “fundamental” might just be a limit case. So the real question is Are particles truly different… or just different expressions of the same underlying structure? Follow for deeper physics that challenges the foundations.
0:10

🚨 BREAKING: Physics just broke its own rulebook. For decades, every particle in the universe fit into 2 types • Bosons (share space) • Fermions (avoid each other) Now? Scientists just created something in between. A new class of particles called anyons that can behave partly like both. Even crazier In 1D systems, you can tune their behavior like a dial between order and exclusion. This means Particle identity isn’t fixed Quantum behavior is adjustable Reality depends on dimensional constraints Think about that. If the rules change based on dimension. then what we call “fundamental” might just be a limit case. So the real question is Are particles truly different… or just different expressions of the same underlying structure? Follow for deeper physics that challenges the foundations.

TheNewPhysics

21,603 просмотров • 4 месяцев назад

If you watch the latest Optimus dancing video in slow motion, you’ll notice that it’s not just dancing, but it’s hopping and balancing on one foot. Also, the string in the back isn’t even holding Tesla Bot up! This level of dynamic balance and control is EXTREMELY difficult to achieve for a humanoid robot. What Tesla is doing right now with Optimus is years ahead of what people realize.
0:15

If you watch the latest Optimus dancing video in slow motion, you’ll notice that it’s not just dancing, but it’s hopping and balancing on one foot. Also, the string in the back isn’t even holding Tesla Bot up! This level of dynamic balance and control is EXTREMELY difficult to achieve for a humanoid robot. What Tesla is doing right now with Optimus is years ahead of what people realize.

Teslaconomics

117,068 просмотров • 1 год назад

JACK JUST ORDERED 2,000 HUMANOID ROBOTS. SOFT FABRIC WAS SUPPOSED TO BE THE ONE THING ROBOTS COULDN’T HANDLE.🤖👕 Sewing has always been the automation-proof job. Cloth wrinkles, shifts, deforms — machines are built for rigid parts, not soft ones. Now watch the clip. Alignment deviations for cut pieces like collars and pockets have to stay within ±2mm. Cutting and sewing motions demand 0.3-0.5mm precision — tighter tolerances than most metal assembly work. Robots on this line are already hitting 98% fabric-separation accuracy, running 3-second operations, and reaching 80% of human efficiency. They’re not alone at the machines. Real workers sit right beside them, running the same Juki and Jack stations, same shift. A Jack executive was upfront: the robots can currently perform individual sewing operations, not run a complete production line independently. This is task automation, not a fully autonomous tailor. But it’s real, and it’s running on an active factory floor. Jack Technology has now ordered 2,000 of these units for apparel manufacturing — aiming to lift manufacturing efficiency by at least 30% across the line. Ten years ago, “robot-proof job” was the whole pitch for garment work. That pitch just expired. Would a 2,000-robot rollout change where your clothes get made?
0:15

JACK JUST ORDERED 2,000 HUMANOID ROBOTS. SOFT FABRIC WAS SUPPOSED TO BE THE ONE THING ROBOTS COULDN’T HANDLE.🤖👕 Sewing has always been the automation-proof job. Cloth wrinkles, shifts, deforms — machines are built for rigid parts, not soft ones. Now watch the clip. Alignment deviations for cut pieces like collars and pockets have to stay within ±2mm. Cutting and sewing motions demand 0.3-0.5mm precision — tighter tolerances than most metal assembly work. Robots on this line are already hitting 98% fabric-separation accuracy, running 3-second operations, and reaching 80% of human efficiency. They’re not alone at the machines. Real workers sit right beside them, running the same Juki and Jack stations, same shift. A Jack executive was upfront: the robots can currently perform individual sewing operations, not run a complete production line independently. This is task automation, not a fully autonomous tailor. But it’s real, and it’s running on an active factory floor. Jack Technology has now ordered 2,000 of these units for apparel manufacturing — aiming to lift manufacturing efficiency by at least 30% across the line. Ten years ago, “robot-proof job” was the whole pitch for garment work. That pitch just expired. Would a 2,000-robot rollout change where your clothes get made?

DN_DEGEN

28,317 просмотров • 20 дней назад

Split-screen test on Flux 3! Prompt : Split-screen video showing the same real-time action from two different camera angles. The screen is divided vertically into two equal halves. On the left side, show a static, wide overhead view from a ceiling-mounted camera in the back corner of the room. The full room layout is visible: a couch, coffee table, rug, bookshelves, and a tall shelf near the wall. The cat is perched on top of the shelf. At the 2-second mark, it suddenly jumps, flips mid-air above the furniture, and lands gracefully on the coffee table. On the right side, show the same action in perfect sync from a side, eye-level camera placed low beside the couch. The view includes the shelf, the space between, and the coffee table. The camera follows the cat’s leap, capturing body motion, twist, and landing.Both sides must be precisely synchronized, showing the exact same event from different angles with consistent lighting, physics, and timing.
0:10

Split-screen test on Flux 3! Prompt : Split-screen video showing the same real-time action from two different camera angles. The screen is divided vertically into two equal halves. On the left side, show a static, wide overhead view from a ceiling-mounted camera in the back corner of the room. The full room layout is visible: a couch, coffee table, rug, bookshelves, and a tall shelf near the wall. The cat is perched on top of the shelf. At the 2-second mark, it suddenly jumps, flips mid-air above the furniture, and lands gracefully on the coffee table. On the right side, show the same action in perfect sync from a side, eye-level camera placed low beside the couch. The view includes the shelf, the space between, and the coffee table. The camera follows the cat’s leap, capturing body motion, twist, and landing.Both sides must be precisely synchronized, showing the exact same event from different angles with consistent lighting, physics, and timing.

Umesh

87,894 просмотров • 1 месяц назад

Traditional factory automation follows three rules: 1. Minimise degrees of freedom 2. Minimise actuation 3. Minimise the requirement for feedback Let geometry and gravity do the hard work. This is a step feeder. Reciprocating plates push parts upward, one step at a time. Parts that sit stable on the ledge rise to the top. Parts that don't? They tip off and fall back into the hopper. A chain conveyor carries the parts along and a simple tool knocks off any that are misaligned. No robot. No vision. No gripper. Just continuous circulation until every part finds its perfect orientation. Bowl feeders, gravity slides, step feeders, escapements—all built on the same principle. Constrain movement. Let shape and weight do the sorting. Modern robotics throws sensors, actuators, and computing at every problem. Sometimes the smarter move is less.
0:11

Traditional factory automation follows three rules: 1. Minimise degrees of freedom 2. Minimise actuation 3. Minimise the requirement for feedback Let geometry and gravity do the hard work. This is a step feeder. Reciprocating plates push parts upward, one step at a time. Parts that sit stable on the ledge rise to the top. Parts that don't? They tip off and fall back into the hopper. A chain conveyor carries the parts along and a simple tool knocks off any that are misaligned. No robot. No vision. No gripper. Just continuous circulation until every part finds its perfect orientation. Bowl feeders, gravity slides, step feeders, escapements—all built on the same principle. Constrain movement. Let shape and weight do the sorting. Modern robotics throws sensors, actuators, and computing at every problem. Sometimes the smarter move is less.

Jack 🤖

467,448 просмотров • 8 месяцев назад

A Carnegie Mellon student launched a Markov Chain model that reads the structure beneath the price and made $67,000 in three weeks on Polymarket. In his applied mathematics classes he studied Markov Chains - the same model physicists used to simulate neutrons in nuclear reactors. The same model describes how prices move on prediction markets. He wrote a Python script. > Takes 60 days of contract price history. > Builds a transition matrix - a map of how the price actually moves between states. > Runs 10,000 simulations in a tenth of a second. > Calibrates the result against data from 72.1 million real trades. The output is an exact probability and position size. No gut feeling. His first test day brought $3,400. A week later he improved the result and hit $17,000. His balance has now reached $67,000. full guide on how to build the same model - in the article below.
0:19

A Carnegie Mellon student launched a Markov Chain model that reads the structure beneath the price and made $67,000 in three weeks on Polymarket. In his applied mathematics classes he studied Markov Chains - the same model physicists used to simulate neutrons in nuclear reactors. The same model describes how prices move on prediction markets. He wrote a Python script. > Takes 60 days of contract price history. > Builds a transition matrix - a map of how the price actually moves between states. > Runs 10,000 simulations in a tenth of a second. > Calibrates the result against data from 72.1 million real trades. The output is an exact probability and position size. No gut feeling. His first test day brought $3,400. A week later he improved the result and hit $17,000. His balance has now reached $67,000. full guide on how to build the same model - in the article below.

Damir Akaza

136,105 просмотров • 3 месяцев назад

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:
0:17

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 просмотров • 9 месяцев назад

⚠️ THIS HOTEL CLEANING CREW HAS A BATTERY PERCENTAGE INSTEAD OF A BREAK TIME Two robots in matching Tesla aprons working a high-rise suite together, one vacuuming the nightstand, one wiping down the floor to ceiling windows, a fully stocked housekeeping cart between them. A status display on the ceiling reads out their battery level and how far into the job they are, like a delivery tracker for a made bed. Here's the honest catch. This is AI-generated concept footage, the onscreen task tracker and the duplicate robots visible in the mirror are giveaways once you look for them, no hotel is running this today. But the job itself is real and well understood, hotel housekeeping is one of the most repetitive, physically demanding jobs in hospitality, and it's exactly the kind of task these companies keep aiming humanoid robots at first. Would you actually want faster, more consistent cleaning if it meant a robot walked through your room while you were out?
0:10

⚠️ THIS HOTEL CLEANING CREW HAS A BATTERY PERCENTAGE INSTEAD OF A BREAK TIME Two robots in matching Tesla aprons working a high-rise suite together, one vacuuming the nightstand, one wiping down the floor to ceiling windows, a fully stocked housekeeping cart between them. A status display on the ceiling reads out their battery level and how far into the job they are, like a delivery tracker for a made bed. Here's the honest catch. This is AI-generated concept footage, the onscreen task tracker and the duplicate robots visible in the mirror are giveaways once you look for them, no hotel is running this today. But the job itself is real and well understood, hotel housekeeping is one of the most repetitive, physically demanding jobs in hospitality, and it's exactly the kind of task these companies keep aiming humanoid robots at first. Would you actually want faster, more consistent cleaning if it meant a robot walked through your room while you were out?

V0LYX

22,127 просмотров • 2 дней назад

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.
0:05

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 просмотров • 6 месяцев назад

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.
0:17

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,390 просмотров • 1 месяц назад

Seedance 2.0 has a fix for character drift almost nobody uses. Every new text description is a fresh interpretation of appearance. That's where the drift comes from: a slightly different face, a slightly different outfit, proportions that shift from scene to scene. The @ Image tag solves this directly — attach a photo once, and it becomes the fixed source of truth for face, outfit, and proportions across the entire multi-shot sequence. The @ Video tag works the same way for motion and camera — tag a reference instead of describing it, and the model copies that exact style instead of an approximation. You're not making the model remember. You're just stopping yourself from reminding it differently every single time.
0:10

Seedance 2.0 has a fix for character drift almost nobody uses. Every new text description is a fresh interpretation of appearance. That's where the drift comes from: a slightly different face, a slightly different outfit, proportions that shift from scene to scene. The @ Image tag solves this directly — attach a photo once, and it becomes the fixed source of truth for face, outfit, and proportions across the entire multi-shot sequence. The @ Video tag works the same way for motion and camera — tag a reference instead of describing it, and the model copies that exact style instead of an approximation. You're not making the model remember. You're just stopping yourself from reminding it differently every single time.

Zentrix⌚️

88,931 просмотров • 1 месяц назад