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UPDATE: Flyctor has a second body now I bought another Vector robot and connected it to the same stack: - a camera feed - GPT-6 Astra for real-time visual understanding - a MaleCNS fly-brain simulation for movement decisions - a small audio protocol for robot-to-robot communication I didn't let... them exchange normal Wi-Fi messages. Instead, each robot has to communicate through its speaker and microphone. One robot emits a short sequence of tones. The other records the sound, converts it into a frequency pattern, classifies the pattern, and injects it into its fly-brain simulation as a sensory event. Right now their entire vocabulary is only four signals: one chirp = "I found an object" two chirps = "come closer" long tone = "path blocked" rapid chirps = "I need help" The interesting part is that the robots don't send coordinates. They don't share a map. They don't know where the other robot is. One Flyctor sees something through Astra, decides it matters, sends a sound, and the other Flyctor has to hear it, interpret it, then decide what to do with that information. camera -> Astra -> fly brain -> speaker microphone -> audio decoder -> fly brain -> movement It is basically a tiny sensory loop between two digital insect nervous systems. Yesterday one robot detected a blocked path and sent the long-tone signal. The other one heard it, turned toward the sound, and rolled over to investigate. It was not impressive in the way modern AI demos are impressive. It was impressive because it felt like watching two small creatures notice each other for the first time.show more

kiruwaaaa

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21,000 次观看 • 11 天前 •via X (Twitter)

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HOLY SH*T, THE WIRING FROM A DEAD FLY’S BRAIN IS NOW DRIVING A TINY ROBOT. not a simulation trapped inside a computer. a physical machine receiving camera input and turning it into movement. scientists reconstructed a fly brain containing: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior developers then built a loop: camera frame → modeled neural activity → movement command → robot turns, walks or avoids an obstacle the camera supplies the pixels. the fly’s neural wiring decides which pixels matter. this does not mean the fly was revived. the robot is not conscious. it does not understand its surroundings. and there is no tiny mind trapped inside the machine. but the control system is based on biological wiring that evolution spent millions of years refining. the fly is dead. its solution to movement is now walking around in another body. I broke down how 166,700 neurons became a physical controller below ↓

HOLY SH*T, THE WIRING FROM A DEAD FLY’S BRAIN IS NOW DRIVING A TINY ROBOT. not a simulation trapped inside a computer. a physical machine receiving camera input and turning it into movement. scientists reconstructed a fly brain containing: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior developers then built a loop: camera frame → modeled neural activity → movement command → robot turns, walks or avoids an obstacle the camera supplies the pixels. the fly’s neural wiring decides which pixels matter. this does not mean the fly was revived. the robot is not conscious. it does not understand its surroundings. and there is no tiny mind trapped inside the machine. but the control system is based on biological wiring that evolution spent millions of years refining. the fly is dead. its solution to movement is now walking around in another body. I broke down how 166,700 neurons became a physical controller below ↓

kozh ./

92,936 次观看 • 5 天前

HOLY SH*T, A DEAD FLY’S BRAIN MAP IS NOW MAKING A ROBOT MOVE. not a brain sitting inside a jar. not an AI trained to imitate how a fly behaves. the actual wiring reconstructed from its nervous system. scientists mapped: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior AI helped reconstruct the connections. developers then turned the map into a control loop: camera sees the environment → fly-derived visual circuits process the input → modeled neurons produce activity → activity becomes motor commands → the robot moves the body is synthetic. the control logic begins with biological wiring shaped by millions of years of evolution. this does not mean the fly was resurrected. the robot isn’t conscious. it doesn’t remember being alive. it doesn’t understand where it is going. but the solution its nervous system used to see, react and move can now operate inside a completely different body. this is bigger than one robot. if biological circuits can become inspectable controllers, engineers may not need to invent every intelligent behavior from zero. they can study solutions evolution already built. the fly is dead. its wiring is still making something move. I broke down how 166,700 neurons escaped the brain map and entered a machine below ↓

HOLY SH*T, A DEAD FLY’S BRAIN MAP IS NOW MAKING A ROBOT MOVE. not a brain sitting inside a jar. not an AI trained to imitate how a fly behaves. the actual wiring reconstructed from its nervous system. scientists mapped: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior AI helped reconstruct the connections. developers then turned the map into a control loop: camera sees the environment → fly-derived visual circuits process the input → modeled neurons produce activity → activity becomes motor commands → the robot moves the body is synthetic. the control logic begins with biological wiring shaped by millions of years of evolution. this does not mean the fly was resurrected. the robot isn’t conscious. it doesn’t remember being alive. it doesn’t understand where it is going. but the solution its nervous system used to see, react and move can now operate inside a completely different body. this is bigger than one robot. if biological circuits can become inspectable controllers, engineers may not need to invent every intelligent behavior from zero. they can study solutions evolution already built. the fly is dead. its wiring is still making something move. I broke down how 166,700 neurons escaped the brain map and entered a machine below ↓

kozh ./

37,148 次观看 • 3 天前

LEONARDO, also called LEO, was built by researchers at Caltech’s Center for Autonomous Systems and Technologies. Its full name means LEgs ONboARD drOne. The idea is simple but unusual: • Build a small biped robot • Give it drone-style thrust • Use the legs for ground contact • Use the propellers for balance and lift • Combine walking, hopping and flying in one system LEO is basically a hybrid between a walking robot and a flying drone. How it was built: • Two lightweight legs • Three actuated joints in each leg • Four propeller thrusters near the shoulders • A lightweight body • Leg motors for ground movement • Propellers for balance, lift and aerial control • Real-time control software that synchronizes the legs and propellers How it walks: • The legs move the robot forward • The feet touch the ground like a normal biped • The propellers constantly correct balance from above • The robot can stay upright even in unstable situations • The thrust reduces the risk of falling during difficult motions How it flies: • The legs stop being the main locomotion system • The four propellers generate lift • The robot behaves more like a drone • It can take off, fly over obstacles and land back on its legs What makes it different: • It does not walk like a normal humanoid • It does not fly like a normal drone • It blends both systems • The legs handle contact with the ground • The propellers act like fast stabilizers • The control system decides how much help comes from the legs and how much comes from thrust That is why LEO can: • Walk • Hop • Fly over obstacles • Ride a skateboard • Balance on a slackline The key idea is walking with aerial stabilization.

LEONARDO, also called LEO, was built by researchers at Caltech’s Center for Autonomous Systems and Technologies. Its full name means LEgs ONboARD drOne. The idea is simple but unusual: • Build a small biped robot • Give it drone-style thrust • Use the legs for ground contact • Use the propellers for balance and lift • Combine walking, hopping and flying in one system LEO is basically a hybrid between a walking robot and a flying drone. How it was built: • Two lightweight legs • Three actuated joints in each leg • Four propeller thrusters near the shoulders • A lightweight body • Leg motors for ground movement • Propellers for balance, lift and aerial control • Real-time control software that synchronizes the legs and propellers How it walks: • The legs move the robot forward • The feet touch the ground like a normal biped • The propellers constantly correct balance from above • The robot can stay upright even in unstable situations • The thrust reduces the risk of falling during difficult motions How it flies: • The legs stop being the main locomotion system • The four propellers generate lift • The robot behaves more like a drone • It can take off, fly over obstacles and land back on its legs What makes it different: • It does not walk like a normal humanoid • It does not fly like a normal drone • It blends both systems • The legs handle contact with the ground • The propellers act like fast stabilizers • The control system decides how much help comes from the legs and how much comes from thrust That is why LEO can: • Walk • Hop • Fly over obstacles • Ride a skateboard • Balance on a slackline The key idea is walking with aerial stabilization.

Techniahqrobot | humanoid robots

135,515 次观看 • 3 个月前

$A Stanford student put 166,700 fruit fly neurons in a robot that now writes homework for 30 students. Not one of them has picked up a pen since. The robot is a black aluminum rig with a blue ballpoint, writing square roots and fractions line after line in handwriting that doesn't look like a machine's, while the fly's neurons glow in the corner of the screen. Each student gives it a handwriting sample, and the fly learns the slant, the spacing, the size of the loops and where the letters get lazy at the end of a line, then writes as that student. 30 notebooks, 30 different hands and 1 brain. That brain is smaller than a poppy seed, and a human brain has over 500,000 times more neurons, but the fly doesn't get tired, doesn't smudge the ink and never mixes up whose 7s have a line through them. The teacher checks 30 notebooks, and a fly wrote every one.$

A Stanford student put 166,700 fruit fly neurons in a robot that now writes homework for 30 students. Not one of them has picked up a pen since. The robot is a black aluminum rig with a blue ballpoint, writing square roots and fractions line after line in handwriting that doesn't look like a machine's, while the fly's neurons glow in the corner of the screen. Each student gives it a handwriting sample, and the fly learns the slant, the spacing, the size of the loops and where the letters get lazy at the end of a line, then writes as that student. 30 notebooks, 30 different hands and 1 brain. That brain is smaller than a poppy seed, and a human brain has over 500,000 times more neurons, but the fly doesn't get tired, doesn't smudge the ink and never mixes up whose 7s have a line through them. The teacher checks 30 notebooks, and a fly wrote every one.

Spike 1%

289,140 次观看 • 14 天前

🚨 THIS IS HOW IT STARTS — HUMANOID ROBOT SUDDENLY LUNGES AT A MAN — EXACTLY THE MOMENT PEOPLE FEARED A humanoid robot was casually kicking a soccer ball around during a public demonstration. Then the ball rolled toward a man in the audience. He tried to kick it back. That’s when the robot suddenly charged toward him and raised its arm like it was about to swing. Within seconds, a handler rushed in and grabbed the robot before the moment escalated. People in the room laugh nervously… but the clip is making the rounds online for one reason: The movement looks almost human. The way it runs. The stance. The arm coming up. Even the comments are losing it: • “Tf was it going to HIT for???” • “Why did it need to be held back?” • “DON’T TOUCH MY BALL” • “How do they get pissed off if they supposedly don’t have emotions?” The strangest part? The robot seemed to react the instant the human interfered with the ball. Are we just watching a glitch during a demo… or the exact moment people warned about?

🚨 THIS IS HOW IT STARTS — HUMANOID ROBOT SUDDENLY LUNGES AT A MAN — EXACTLY THE MOMENT PEOPLE FEARED A humanoid robot was casually kicking a soccer ball around during a public demonstration. Then the ball rolled toward a man in the audience. He tried to kick it back. That’s when the robot suddenly charged toward him and raised its arm like it was about to swing. Within seconds, a handler rushed in and grabbed the robot before the moment escalated. People in the room laugh nervously… but the clip is making the rounds online for one reason: The movement looks almost human. The way it runs. The stance. The arm coming up. Even the comments are losing it: • “Tf was it going to HIT for???” • “Why did it need to be held back?” • “DON’T TOUCH MY BALL” • “How do they get pissed off if they supposedly don’t have emotions?” The strangest part? The robot seemed to react the instant the human interfered with the ball. Are we just watching a glitch during a demo… or the exact moment people warned about?

HustleBitch

54,191 次观看 • 6 个月前

⚠️ THIS ROBOT ANSWERED A HOSTILE QUESTION IN 9 WORDS. THAT'S THE SPEC SHEET NOBODY IS READING. "To make humans lives easier and a little more interesting." That answer wasn't charming. It was precise. Break it down: → Addresses utility (easier) → Addresses engagement (more interesting) → Centers the human, not the robot → Delivered in under 10 words to a hostile prompt That is a mission statement that survives contact with a skeptic. In public. Unrehearsed. Procurement teams building out human-robot workflows don't need the robot to be impressive. They need it to be explainable, to workers, to floor managers, to anyone who stops it and asks why it exists. This robot just passed that test. Nobody runs that number on a demo reel. This one ran it live.

⚠️ THIS ROBOT ANSWERED A HOSTILE QUESTION IN 9 WORDS. THAT'S THE SPEC SHEET NOBODY IS READING. "To make humans lives easier and a little more interesting." That answer wasn't charming. It was precise. Break it down: → Addresses utility (easier) → Addresses engagement (more interesting) → Centers the human, not the robot → Delivered in under 10 words to a hostile prompt That is a mission statement that survives contact with a skeptic. In public. Unrehearsed. Procurement teams building out human-robot workflows don't need the robot to be impressive. They need it to be explainable, to workers, to floor managers, to anyone who stops it and asks why it exists. This robot just passed that test. Nobody runs that number on a demo reel. This one ran it live.

Shredder

203,661 次观看 • 1 个月前

this is insane, a dead fly brain just got a new body 166,700 fruit fly neurons and more than 25 million biological connections are now sitting inside a robotic body that looks almost exactly like the animal they came from the lab gave those neurons cameras, wings and motors, then let the system figure out what those signals meant without writing normal flight behavior for it a light moves and the head turns airflow changes and the wings correct another fly crosses the camera and the robot starts following it the creepy part is how little machinery the brain actually needs before old instincts start becoming useful again nature spent hundreds of millions of years building a nervous system that can react, stabilize and chase movement someone just gave that nervous system a body made of metal the original fly is gone its wiring is still trying to fly

this is insane, a dead fly brain just got a new body 166,700 fruit fly neurons and more than 25 million biological connections are now sitting inside a robotic body that looks almost exactly like the animal they came from the lab gave those neurons cameras, wings and motors, then let the system figure out what those signals meant without writing normal flight behavior for it a light moves and the head turns airflow changes and the wings correct another fly crosses the camera and the robot starts following it the creepy part is how little machinery the brain actually needs before old instincts start becoming useful again nature spent hundreds of millions of years building a nervous system that can react, stabilize and chase movement someone just gave that nervous system a body made of metal the original fly is gone its wiring is still trying to fly

explos1ve

196,313 次观看 • 13 天前

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 次观看 • 1 个月前

Part two of the jeans robot. Update: it's moving. Just a miniature, and only one leg, but I already feel an absurd amount of love for it. Can't wait for it to start breaking in its first pair.

Part two of the jeans robot. Update: it's moving. Just a miniature, and only one leg, but I already feel an absurd amount of love for it. Can't wait for it to start breaking in its first pair.

Kunal Kapoor

59,012 次观看 • 2 个月前

A FLY’S VISUAL SYSTEM COULD BECOME A ROBOT’S EYES. not by copying the eyes. by copying the wiring behind them. a fly’s brain doesn’t build a detailed picture of the world. its visual circuits reduce light, contrast and motion into decisions: something moved obstacle ahead turn keep going AI helped reconstruct a map containing 166,700 neurons and roughly 125 million synapses. developers can take the visual pathways from that map and build a loop: camera frame → modeled neural activity → movement command → robot the camera supplies the pixels. the fly’s wiring decides which pixels matter. this does not mean the robot sees, understands or thinks like a fly. but it shows something more useful: millions of years of biological visual processing can become an inspectable control system. the fly is dead. its solution to vision doesn’t have to die with it. I broke down how biological wiring could become a robot’s navigation system below ↓

A FLY’S VISUAL SYSTEM COULD BECOME A ROBOT’S EYES. not by copying the eyes. by copying the wiring behind them. a fly’s brain doesn’t build a detailed picture of the world. its visual circuits reduce light, contrast and motion into decisions: something moved obstacle ahead turn keep going AI helped reconstruct a map containing 166,700 neurons and roughly 125 million synapses. developers can take the visual pathways from that map and build a loop: camera frame → modeled neural activity → movement command → robot the camera supplies the pixels. the fly’s wiring decides which pixels matter. this does not mean the robot sees, understands or thinks like a fly. but it shows something more useful: millions of years of biological visual processing can become an inspectable control system. the fly is dead. its solution to vision doesn’t have to die with it. I broke down how biological wiring could become a robot’s navigation system below ↓

kozh ./

81,861 次观看 • 11 天前

A dead fruit fly was sliced into 66 pieces. Now its 166,700 neurons are driving robots. Nobody trained it, no dataset, no reward function, no GPU farm, just the wiring. The fly took a second to die, but copying it took 7 microscopes running nonstop for a year, shaving it 1/10,000 of a hair at a time and photographing every layer before the next cut. AI rebuilt it from the scraps, eyes to legs with the neck still attached, 124M synapses and 11,710 cell types, 44 years of human labor finished by machines. Scientists first tried this in 2008 and guessed it would take 500 people 10 years. Then the internet got the file and someone gave it a body. Camera → fly eyes → 166,700 spiking neurons → descending neurons → motors. One of those neurons is DNp01, the giant fiber, the cell that fires when your hand comes down. Push an object at the camera and the looming detectors light up, so the robot swerves. It took 0 training runs, just the same reflex that gets a fly out from under a swatter in 100 milliseconds. Then came the control test, where they kept all 138,639 neurons and shuffled where the synapses connect. The behavior died. The intelligence was never in the neurons, it was in the map. The fly is gone, but the flinch is still running.

A dead fruit fly was sliced into 66 pieces. Now its 166,700 neurons are driving robots. Nobody trained it, no dataset, no reward function, no GPU farm, just the wiring. The fly took a second to die, but copying it took 7 microscopes running nonstop for a year, shaving it 1/10,000 of a hair at a time and photographing every layer before the next cut. AI rebuilt it from the scraps, eyes to legs with the neck still attached, 124M synapses and 11,710 cell types, 44 years of human labor finished by machines. Scientists first tried this in 2008 and guessed it would take 500 people 10 years. Then the internet got the file and someone gave it a body. Camera → fly eyes → 166,700 spiking neurons → descending neurons → motors. One of those neurons is DNp01, the giant fiber, the cell that fires when your hand comes down. Push an object at the camera and the looming detectors light up, so the robot swerves. It took 0 training runs, just the same reflex that gets a fly out from under a swatter in 100 milliseconds. Then came the control test, where they kept all 138,639 neurons and shuffled where the synapses connect. The behavior died. The intelligence was never in the neurons, it was in the map. The fly is gone, but the flinch is still running.

Spike 1%

24,336 次观看 • 8 天前

I watched Robot on the Road and honestly I did not really know what to expect going in At the beginning it just feels like a pretty normal little road trip setup with this robot hitchhiking around Japan and it almost gives off a chill slice of life vibe But it does not stay like that for long The more it goes on the more you realize the robot is doing some really questionable stuff and the way it focuses on women without their consent makes the whole thing feel really uncomfortable By the end I was not really sure if I was supposed to find it funny or just feel weird about it Overall it feels like one of those shorts that tries to mix dark humor with awkward situations but it mostly just ends up being unsettling

I watched Robot on the Road and honestly I did not really know what to expect going in At the beginning it just feels like a pretty normal little road trip setup with this robot hitchhiking around Japan and it almost gives off a chill slice of life vibe But it does not stay like that for long The more it goes on the more you realize the robot is doing some really questionable stuff and the way it focuses on women without their consent makes the whole thing feel really uncomfortable By the end I was not really sure if I was supposed to find it funny or just feel weird about it Overall it feels like one of those shorts that tries to mix dark humor with awkward situations but it mostly just ends up being unsettling

Anime Posts

6,565,525 次观看 • 3 个月前

What stuck with me in 2022 is that the Roomba photos weren’t leaked. They were sent out normally, as intended, to contractors who were paid to review them one by one. A woman sitting in the bathroom of her own home ended up in a Facebook group because "someone thought it was funny". Matic can’t do that even if it wanted to. The processing stays inside the robot. Cut the Wi-Fi and it keeps cleaning. It’s the first time you’re not being asked to take a company at its word.

What stuck with me in 2022 is that the Roomba photos weren’t leaked. They were sent out normally, as intended, to contractors who were paid to review them one by one. A woman sitting in the bathroom of her own home ended up in a Facebook group because "someone thought it was funny". Matic can’t do that even if it wanted to. The processing stays inside the robot. Cut the Wi-Fi and it keeps cleaning. It’s the first time you’re not being asked to take a company at its word.

Kayla ♔

92,799 次观看 • 1 个月前

1. Dr. René Peoc'h built a robot that turned randomly left and right, completely at random as it moved through an arena. As a control, he recorded its path with no chicks present. It covered the arena evenly. Exactly as probability predicted. Then he introduced newly hatched chicks who had imprinted on the robot as their mother. He put them in a cage on one side of the arena. They could see the robot. They couldn't move toward it. 2. What happened defied physics. The robot still moving completely at random, with no modifications began spending the majority of its time in the half of the arena closest to the chicks. The sheer intention of the baby chicks wanting to be near what they believed was their mother, influenced the movement of a computerized machine. The study was published. Peer-reviewed. Replicated. 3. This is not mysticism. This is quantum mechanics. At the subatomic level, matter does not exist as a fixed thing. Electrons exist as waves of possibility infinite potential until they are observed. The moment a conscious observer focuses attention on a quantum possibility, it collapses from energy into matter. This is called collapsing the wave function. It is not metaphor. It is the foundation of quantum physics. 4. What Dr. Joe Dispenza's research shows: When a person enters a genuine state of elevated emotion: gratitude, joy, love while holding a clear intention for their future, they change their electromagnetic signature. When that signature matches the frequency of a potential that already exists in the quantum field. The experience finds them. Without effort. Without forcing. His son. A student who won a lottery ticket. Tumors that disappeared. New jobs that arrived without applications. Thousands of documented cases. 5. You are not separate from what you want to create. You are the antenna. And what you broadcast through the quality of your thoughts and the elevation of your emotions is what reality responds to. The chicks didn't move toward the robot. The robot moved toward them.

1. Dr. René Peoc'h built a robot that turned randomly left and right, completely at random as it moved through an arena. As a control, he recorded its path with no chicks present. It covered the arena evenly. Exactly as probability predicted. Then he introduced newly hatched chicks who had imprinted on the robot as their mother. He put them in a cage on one side of the arena. They could see the robot. They couldn't move toward it. 2. What happened defied physics. The robot still moving completely at random, with no modifications began spending the majority of its time in the half of the arena closest to the chicks. The sheer intention of the baby chicks wanting to be near what they believed was their mother, influenced the movement of a computerized machine. The study was published. Peer-reviewed. Replicated. 3. This is not mysticism. This is quantum mechanics. At the subatomic level, matter does not exist as a fixed thing. Electrons exist as waves of possibility infinite potential until they are observed. The moment a conscious observer focuses attention on a quantum possibility, it collapses from energy into matter. This is called collapsing the wave function. It is not metaphor. It is the foundation of quantum physics. 4. What Dr. Joe Dispenza's research shows: When a person enters a genuine state of elevated emotion: gratitude, joy, love while holding a clear intention for their future, they change their electromagnetic signature. When that signature matches the frequency of a potential that already exists in the quantum field. The experience finds them. Without effort. Without forcing. His son. A student who won a lottery ticket. Tumors that disappeared. New jobs that arrived without applications. Thousands of documented cases. 5. You are not separate from what you want to create. You are the antenna. And what you broadcast through the quality of your thoughts and the elevation of your emotions is what reality responds to. The chicks didn't move toward the robot. The robot moved toward them.

🧬Maxpein🧬

35,310 次观看 • 17 天前

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.

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 • 7 个月前

a real fly brain saw its 40th video 3 seconds ago. it did not choose any of them. it never will. that's FLYTOK. I open-sourced a live simulation of a real fruit fly connectome and gave it a phone. here's exactly what 166,700 neurons do while you scroll: THE WIRING MaleCNS v1.0. the reconstructed central nervous system of a real male Drosophila. 166,700 neurons. 25,582,938 directed connections. 124,177,617 synaptic contacts. nothing cropped. no "simplified 1,000-neuron version" running behind the scenes. the whole graph is in memory on every single step. THE LOCK the app checksums the connectome against SHA-256 before it's allowed to boot. one array off by a byte and the brain refuses to start. you are either watching the real MaleCNS graph or you are watching nothing. THE SPIKES every neuron is a leaky integrate-and-fire cell. crosses −45 mV, it fires. the spike lands 1.8 ms later. 2.2 ms of silence, then it's live again. the network advances in 0.1 ms steps inside a compiled C++ kernel. roughly 1.2 million spikes per simulated second. THE EYES the browser captures the phone at 90×160 pixels. brightness drives 3,335 R1–R6 photoreceptors. color drives 811 R8 cells, blue and green. each one is placed on the screen by inferring its eye column from its real connections onto L1, L2, L3. real input, through real visual wiring. it's measurable. same brain, same saved state. 100 ms of white produced 127,378 spikes. black produced 92,952. the screen changes the brain. THE ALGORITHM while a video plays, I inject a fixed current into all 15 PAM11 dopamine neurons in the mushroom body. that's it. that's the whole trick. matched 200 ms control: 0 PAM11 spikes. drive on: 261. live, it sits at 85–97 Hz for as long as the feed runs. the fly does not earn it. an app decided watching one more should feel good. THE PLASTICITY 7,835 Kenyon-cell connections onto MBON07 and MBON11 can change. dopamine fires while Kenyon cells were active, those synapses weaken, floored at 10% and never flipping sign. after one stimulated run, 3,087 of them no longer matched their original weight. freeze it and they stay put. restore a checkpoint, replay the input, identical spikes bit for bit. THE SWIPE every 3 seconds the front right leg reaches up and strokes the screen, on the same 900 ms timeline as the video transition. wings, legs and head turns come from actual motor firing, MN9, DNp09, left-vs-right DNa02. those are real. the swipe is not. the fly never decides to swipe. the feed decides for it. ONE BRAIN this isn't a video of a simulation. it's the simulation. there is exactly one brain on one server. whoever loads the page first becomes its eyes and supplies the frames. everyone else watches the same brain react in real time. no video, no input, it just waits. no data, no numbers, the overlay shows nothing. it never invents a reading to look alive. THE HONEST PART the wiring is real. the physiology is approximate. the eyes are approximate. the dopamine is artificial. the feed is on a timer. the learning rule is unvalidated on this connectome. nothing here shows the fly enjoys it, prefers it, is addicted, or experiences anything at all. no living fly was involved. it took a few hundred lines of glue code to build a loop out of a screen, a timer and a reward signal wired to the right cells. imagine what an industry with a budget can do. open source. MaleCNS under CC BY 4.0. runs on your own machine, 16 GB RAM and a few GB of disk. the fly is still scrolling. nobody is coming to take the phone away. links in the reply.

a real fly brain saw its 40th video 3 seconds ago. it did not choose any of them. it never will. that's FLYTOK. I open-sourced a live simulation of a real fruit fly connectome and gave it a phone. here's exactly what 166,700 neurons do while you scroll: THE WIRING MaleCNS v1.0. the reconstructed central nervous system of a real male Drosophila. 166,700 neurons. 25,582,938 directed connections. 124,177,617 synaptic contacts. nothing cropped. no "simplified 1,000-neuron version" running behind the scenes. the whole graph is in memory on every single step. THE LOCK the app checksums the connectome against SHA-256 before it's allowed to boot. one array off by a byte and the brain refuses to start. you are either watching the real MaleCNS graph or you are watching nothing. THE SPIKES every neuron is a leaky integrate-and-fire cell. crosses −45 mV, it fires. the spike lands 1.8 ms later. 2.2 ms of silence, then it's live again. the network advances in 0.1 ms steps inside a compiled C++ kernel. roughly 1.2 million spikes per simulated second. THE EYES the browser captures the phone at 90×160 pixels. brightness drives 3,335 R1–R6 photoreceptors. color drives 811 R8 cells, blue and green. each one is placed on the screen by inferring its eye column from its real connections onto L1, L2, L3. real input, through real visual wiring. it's measurable. same brain, same saved state. 100 ms of white produced 127,378 spikes. black produced 92,952. the screen changes the brain. THE ALGORITHM while a video plays, I inject a fixed current into all 15 PAM11 dopamine neurons in the mushroom body. that's it. that's the whole trick. matched 200 ms control: 0 PAM11 spikes. drive on: 261. live, it sits at 85–97 Hz for as long as the feed runs. the fly does not earn it. an app decided watching one more should feel good. THE PLASTICITY 7,835 Kenyon-cell connections onto MBON07 and MBON11 can change. dopamine fires while Kenyon cells were active, those synapses weaken, floored at 10% and never flipping sign. after one stimulated run, 3,087 of them no longer matched their original weight. freeze it and they stay put. restore a checkpoint, replay the input, identical spikes bit for bit. THE SWIPE every 3 seconds the front right leg reaches up and strokes the screen, on the same 900 ms timeline as the video transition. wings, legs and head turns come from actual motor firing, MN9, DNp09, left-vs-right DNa02. those are real. the swipe is not. the fly never decides to swipe. the feed decides for it. ONE BRAIN this isn't a video of a simulation. it's the simulation. there is exactly one brain on one server. whoever loads the page first becomes its eyes and supplies the frames. everyone else watches the same brain react in real time. no video, no input, it just waits. no data, no numbers, the overlay shows nothing. it never invents a reading to look alive. THE HONEST PART the wiring is real. the physiology is approximate. the eyes are approximate. the dopamine is artificial. the feed is on a timer. the learning rule is unvalidated on this connectome. nothing here shows the fly enjoys it, prefers it, is addicted, or experiences anything at all. no living fly was involved. it took a few hundred lines of glue code to build a loop out of a screen, a timer and a reward signal wired to the right cells. imagine what an industry with a budget can do. open source. MaleCNS under CC BY 4.0. runs on your own machine, 16 GB RAM and a few GB of disk. the fly is still scrolling. nobody is coming to take the phone away. links in the reply.

sopersone

37,608 次观看 • 20 天前

scientists published the complete wiring diagram of a fly's brain 140,000 neurons. 50 million connections. Someone just built an entire trading bot on top of it. they named it FlySwarm. $55 → $8,740 in 28 hours i downloaded it. told it to trade. more than +15,800% profit the fly has been dead for years. its brain is still making money. a fly's brain evolved one job over 50 million years: survive. find food before something kills you. never stop scanning. act in milliseconds. every 8 minutes: runs the full connectome against live market data the way the fly would run it against a field the "food detection" circuits flag where price is moving before volume confirms it the "threat avoidance" circuits kill any position the moment the pattern breaks the "escape reflex" closes everything in under 200 milliseconds if volatility spikes it doesn't calculate. it reacts. the way a fly reacts. it has never hesitated once. night one it dropped to $9. the escape reflex triggered 40 times in 6 hours. a fly survives by being faster than everything trying to eat it. by morning it had rebuilt the balance and was up 15,800%. the market tried to eat it on night one. it flew away. the algo ran for free. it's been running for 50 million years. $55 → $8,740 in 28 hours what does a fly know about markets that we don't?

scientists published the complete wiring diagram of a fly's brain 140,000 neurons. 50 million connections. Someone just built an entire trading bot on top of it. they named it FlySwarm. $55 → $8,740 in 28 hours i downloaded it. told it to trade. more than +15,800% profit the fly has been dead for years. its brain is still making money. a fly's brain evolved one job over 50 million years: survive. find food before something kills you. never stop scanning. act in milliseconds. every 8 minutes: runs the full connectome against live market data the way the fly would run it against a field the "food detection" circuits flag where price is moving before volume confirms it the "threat avoidance" circuits kill any position the moment the pattern breaks the "escape reflex" closes everything in under 200 milliseconds if volatility spikes it doesn't calculate. it reacts. the way a fly reacts. it has never hesitated once. night one it dropped to $9. the escape reflex triggered 40 times in 6 hours. a fly survives by being faster than everything trying to eat it. by morning it had rebuilt the balance and was up 15,800%. the market tried to eat it on night one. it flew away. the algo ran for free. it's been running for 50 million years. $55 → $8,740 in 28 hours what does a fly know about markets that we don't?

Nekt0

47,348 次观看 • 17 天前

$166,000 neurons from a dead fruit fly now hunt drones from a bedroom desk. A white drone the size of a palm lifts off the carpet, and a black 3D-printed turret on the desk swings after it, with the fly's brain working its 2 stepper motors, 1 camera and 2 barrels through a nest of rainbow wires on a breadboard. That brain sits in the top-left corner of the screen and flares every time the drone jerks. A Patriot interceptor costs about $4 million, and armies are firing them at drones worth a fraction of that. This brain is smaller than a poppy seed, and Google put it online for free 2 weeks ago. Then someone gave it a job it was built for. Most of a fly's brain exists to see motion, and this brain is male, and male flies spend their lives chasing whatever moves. The drone climbs past the ceiling fan and the crosshair climbs with it, then drops when it drops. For 18 seconds it never loses the drone. In the last shot the drone flies home to the guy holding the controller, and the crosshair follows it right onto his chest. It spent its whole life dodging swatters. Now it's the one aiming.$

166,000 neurons from a dead fruit fly now hunt drones from a bedroom desk. A white drone the size of a palm lifts off the carpet, and a black 3D-printed turret on the desk swings after it, with the fly's brain working its 2 stepper motors, 1 camera and 2 barrels through a nest of rainbow wires on a breadboard. That brain sits in the top-left corner of the screen and flares every time the drone jerks. A Patriot interceptor costs about $4 million, and armies are firing them at drones worth a fraction of that. This brain is smaller than a poppy seed, and Google put it online for free 2 weeks ago. Then someone gave it a job it was built for. Most of a fly's brain exists to see motion, and this brain is male, and male flies spend their lives chasing whatever moves. The drone climbs past the ceiling fan and the crosshair climbs with it, then drops when it drops. For 18 seconds it never loses the drone. In the last shot the drone flies home to the guy holding the controller, and the crosshair follows it right onto his chest. It spent its whole life dodging swatters. Now it's the one aiming.

Spike 1%

36,660 次观看 • 13 天前

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 次观看 • 2 个月前

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

495,322 次观看 • 1 个月前