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We built a Spiking Neural Network (SNN) to fully control a physical robot arm (6-DOF), and it's running entirely on a Raspberry Pi (no HAT). In our approach, the SNN starts untrained and drives the servos directly, learning in real time through local learning rules (no backprop or gradient...

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Alexander A. Wolf

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30,367 görüntüleme • 7 ay önce •via X (Twitter)

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Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.
2:48

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

385,623 görüntüleme • 1 yıl önce

🚨🇺🇸FIGURE ROBOT LEARNS TO WALK IN HOURS Turns out you can teach a robot to walk—if you’ve got a few years to burn in a physics simulator (or a few hours of GPU time). Figure’s team trained a neural network using reinforcement learning to mimic natural human locomotion, and the wild part is it worked on the first try with real hardware. No tuning. No fiddling. Just plug and strut. Thanks to sim-to-real transfer and some sneaky domain randomization, the robot didn’t just wobble out of the lab—it walked like it had somewhere to be. Next up: running. Not terrifying at all. Source: Figure
0:54

🚨🇺🇸FIGURE ROBOT LEARNS TO WALK IN HOURS Turns out you can teach a robot to walk—if you’ve got a few years to burn in a physics simulator (or a few hours of GPU time). Figure’s team trained a neural network using reinforcement learning to mimic natural human locomotion, and the wild part is it worked on the first try with real hardware. No tuning. No fiddling. Just plug and strut. Thanks to sim-to-real transfer and some sneaky domain randomization, the robot didn’t just wobble out of the lab—it walked like it had somewhere to be. Next up: running. Not terrifying at all. Source: Figure

Mario Nawfal

117,866 görüntüleme • 1 yıl önce

$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.
0:20

$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

Frank

203,581 görüntüleme • 1 gün önce

This robot now carries its own brain — a Qualcomm Dragonwing™ QCS6490 mounted right on its back. No more waiting on the cloud to make sense of the world. Everything processed on the spot, in real time. That's SR Agentic at work.
0:25

This robot now carries its own brain — a Qualcomm Dragonwing™ QCS6490 mounted right on its back. No more waiting on the cloud to make sense of the world. Everything processed on the spot, in real time. That's SR Agentic at work.

Strike Robot

39,149 görüntüleme • 2 ay önce

Today I realized something absolutely with BitRobot Network You dont need to own a robot… to actually control one I was just sitting at my laptop and somehow I was moving a real robotic arm in real time At first I thought it would feel like a video game It didnt Theres a completely different feeling when you know this is happening in the real world not just on a screen Every movement matters. Every action generates data And then it hit me… This is how AI is learning to interact with the real world Not from code alone but from people like us Platforms like BitRobot are opening this to everyone No setup No cost No experience Just curiosity and the internet Someone sitting at home can now help train the next generation of robotics. The future isnt coming its happening right now. If you could control a robot right now what would you try first? 🤖👇
0:26

Today I realized something absolutely with BitRobot Network You dont need to own a robot… to actually control one I was just sitting at my laptop and somehow I was moving a real robotic arm in real time At first I thought it would feel like a video game It didnt Theres a completely different feeling when you know this is happening in the real world not just on a screen Every movement matters. Every action generates data And then it hit me… This is how AI is learning to interact with the real world Not from code alone but from people like us Platforms like BitRobot are opening this to everyone No setup No cost No experience Just curiosity and the internet Someone sitting at home can now help train the next generation of robotics. The future isnt coming its happening right now. If you could control a robot right now what would you try first? 🤖👇

RAJU

40,141 görüntüleme • 5 ay önce

I built this little #robot I ordered from Hugging Face Pollen Robotics and connected it to the Google Gemini AI model through its API gateway. The robot wanted its own personality - so I gave it an #ATX (Texas) vibe and you can see the result in the video. Really awesome - it even answers questions and carries on a conversion. The vision and hearing are great - it can identify and follow what it sees (with a reasonable delay). My next goal is to get it to work with a 100% local AI model. Specs: 2 cameras, 4 microphones, a speaker, and a wifi interface to a Raspberry Pi built-in controller. I plan to demo it at the InnoTech #InnoTechAUS event on May 12th. Texas McCombs Austin Tech Council TechRanch
2:44

I built this little #robot I ordered from Hugging Face Pollen Robotics and connected it to the Google Gemini AI model through its API gateway. The robot wanted its own personality - so I gave it an #ATX (Texas) vibe and you can see the result in the video. Really awesome - it even answers questions and carries on a conversion. The vision and hearing are great - it can identify and follow what it sees (with a reasonable delay). My next goal is to get it to work with a 100% local AI model. Specs: 2 cameras, 4 microphones, a speaker, and a wifi interface to a Raspberry Pi built-in controller. I plan to demo it at the InnoTech #InnoTechAUS event on May 12th. Texas McCombs Austin Tech Council TechRanch

Russ Finney

29,103 görüntüleme • 4 ay önce

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

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

Khurram Javed

52,110 görüntüleme • 9 ay önce

THE FLY BRAIN ESCAPED THE COMPUTER. I PUT IT INSIDE A PHYSICAL ROBOT. Scientists released a reconstructed map containing 166,700 neurons from a fruit fly. I connected it to a Strandbeest. The panel in the top-right shows the simulated brain running. Its neural activity is translated into motor commands that move the mechanical legs below. The catch: this is a computational model built from biological wiring. I engineered the interface connecting its outputs to the robot. First, the fly brain entered DOOM. Then it played Chrome Dino. Now it has a physical body. 166,700 neurons. 25 million connections. One nervous system walking through the real world.
0:53

THE FLY BRAIN ESCAPED THE COMPUTER. I PUT IT INSIDE A PHYSICAL ROBOT. Scientists released a reconstructed map containing 166,700 neurons from a fruit fly. I connected it to a Strandbeest. The panel in the top-right shows the simulated brain running. Its neural activity is translated into motor commands that move the mechanical legs below. The catch: this is a computational model built from biological wiring. I engineered the interface connecting its outputs to the robot. First, the fly brain entered DOOM. Then it played Chrome Dino. Now it has a physical body. 166,700 neurons. 25 million connections. One nervous system walking through the real world.

Frank

4,799,634 görüntüleme • 2 gün önce

Squaring The Face Controlling your golf ball is all about managing the club face. Understanding the how the arms will influence the club face is key. The lead arm will have some internal rotation in the backswing, therefore we will need to add external rotation to this arm through the release. If we fail to do this, we will leave the face open and often bring in more chicken wing/scoop patterns through contact. Learning to externally rotate the lead arm will help you keep a flat lead wrist and deliver a stronger loft. If you’re a player who tends to slice or fade the ball, this concept will help you to feel what you need to square it up. I just posted the full version of this lesson on the ProShopApp!!! Comment “SQUARE” if you’d like access to the video.
1:29

Squaring The Face Controlling your golf ball is all about managing the club face. Understanding the how the arms will influence the club face is key. The lead arm will have some internal rotation in the backswing, therefore we will need to add external rotation to this arm through the release. If we fail to do this, we will leave the face open and often bring in more chicken wing/scoop patterns through contact. Learning to externally rotate the lead arm will help you keep a flat lead wrist and deliver a stronger loft. If you’re a player who tends to slice or fade the ball, this concept will help you to feel what you need to square it up. I just posted the full version of this lesson on the ProShopApp!!! Comment “SQUARE” if you’d like access to the video.

Alex Riggs

56,887 görüntüleme • 1 yıl önce

This is one-shot assembly: you show examples of what to build, and the robot just does it. (see original post: To share more on how this works, the robot is controlled in real time by a neural network that takes in video pixels and outputs 100Hz actions. The video below is part of the raw input passed directly into the model. I also like this view (at 1x speed) because it shows more of the (I think very cool) subtle moments of dexterity near the fingertips 👌 One-shot assembly seemed like a dream even just a year ago — it's not easy. It requires both the high-level reasoning of "what to build" (recognizing the geometry of the structures presented by the human), and the low-level visuomotor control of "how to build it" (purposefully re-orienting individual pieces and nudging them together in place). While possible to manually engineer a complex system for this (e.g. w/ hierarchical control, or explicit state representations), we were curious if our own Foundation model could do it all end-to-end with just some post-training data. Surprisingly, it just worked. Nothing about the recipe is substantially different than any other demo we’ve run in the past, and we’re excited about its implications on model capabilities: • On contextual reasoning, these models can (i) attend to task-related pixels in the peripheral view of the video inputs, and (ii) retain this knowledge in-context while ignoring irrelevant background. This is useful for generalizing to a wide range of real workflows: e.g. paying attention to what’s coming down the conveyor line, or glancing at the instructions displayed on a nearby monitor. • On dexterity, these models can produce contact-rich "commonsense" behaviors that can be difficult to pre-program or write language instructions for e.g. rolling a brick slightly to align its studs against the bottom of another, re-grasping to get a better grip or to move out of the way before a forceful press, or gently pushing the corners of a brick against the mat to rotate it in hand and stand it up vertically (i.e. extrinsic dexterity). These aspects work together to form a capability that resembles fast adaptation — a hallmark of intelligence, relevant for real use cases. This has also expanded my own perspective on what's possible with robot learning, using a recipe that's repeatable for many more skills. This milestone stands on top of the solid technical foundations we’ve built here at Generalist: hardcore controls & hardware, all in-house built models, and a data engine that "just works." We're a small group of hyper-focused engineers, and hands-down the highest talent-density team I’ve ever worked with. We're accelerating and scaling aggressively towards unlocking next-generation robot intelligence. Building Legos is just one example, and it's clear to me that we're headed towards a future where robots can do just about anything we want them to. Its coming, and we're going to make it happen.
1:01

This is one-shot assembly: you show examples of what to build, and the robot just does it. (see original post: To share more on how this works, the robot is controlled in real time by a neural network that takes in video pixels and outputs 100Hz actions. The video below is part of the raw input passed directly into the model. I also like this view (at 1x speed) because it shows more of the (I think very cool) subtle moments of dexterity near the fingertips 👌 One-shot assembly seemed like a dream even just a year ago — it's not easy. It requires both the high-level reasoning of "what to build" (recognizing the geometry of the structures presented by the human), and the low-level visuomotor control of "how to build it" (purposefully re-orienting individual pieces and nudging them together in place). While possible to manually engineer a complex system for this (e.g. w/ hierarchical control, or explicit state representations), we were curious if our own Foundation model could do it all end-to-end with just some post-training data. Surprisingly, it just worked. Nothing about the recipe is substantially different than any other demo we’ve run in the past, and we’re excited about its implications on model capabilities: • On contextual reasoning, these models can (i) attend to task-related pixels in the peripheral view of the video inputs, and (ii) retain this knowledge in-context while ignoring irrelevant background. This is useful for generalizing to a wide range of real workflows: e.g. paying attention to what’s coming down the conveyor line, or glancing at the instructions displayed on a nearby monitor. • On dexterity, these models can produce contact-rich "commonsense" behaviors that can be difficult to pre-program or write language instructions for e.g. rolling a brick slightly to align its studs against the bottom of another, re-grasping to get a better grip or to move out of the way before a forceful press, or gently pushing the corners of a brick against the mat to rotate it in hand and stand it up vertically (i.e. extrinsic dexterity). These aspects work together to form a capability that resembles fast adaptation — a hallmark of intelligence, relevant for real use cases. This has also expanded my own perspective on what's possible with robot learning, using a recipe that's repeatable for many more skills. This milestone stands on top of the solid technical foundations we’ve built here at Generalist: hardcore controls & hardware, all in-house built models, and a data engine that "just works." We're a small group of hyper-focused engineers, and hands-down the highest talent-density team I’ve ever worked with. We're accelerating and scaling aggressively towards unlocking next-generation robot intelligence. Building Legos is just one example, and it's clear to me that we're headed towards a future where robots can do just about anything we want them to. Its coming, and we're going to make it happen.

Andy Zeng

49,443 görüntüleme • 11 ay önce

Reachy Mini just got a new Brain! We released a fully open-source backend for talking to Reachy Mini. In the last 48 hours, 3,000+ robots have already hit the version deployed on Hugging Face's infra. Until today, if you wanted a real-time voice agent in your robot, you were looking at $20+ a day. Realtime APIs are expensive because every second the connection is up, you are being charged! But with our approach, you can run the audio models locally and use your OpenAI or Claude subscription for the LLM, at no extra costs. The audio pipeline is very efficient; in the video, it's running on the cheapest Mac mini, 16GB of RAM (the one that sold out a couple of months back when people were buying them up for openclaws setups). Tutorials coming in the next few weeks: how to run your robot fully locally, or with your favorite agent. Stay tuned!
1:50

Reachy Mini just got a new Brain! We released a fully open-source backend for talking to Reachy Mini. In the last 48 hours, 3,000+ robots have already hit the version deployed on Hugging Face's infra. Until today, if you wanted a real-time voice agent in your robot, you were looking at $20+ a day. Realtime APIs are expensive because every second the connection is up, you are being charged! But with our approach, you can run the audio models locally and use your OpenAI or Claude subscription for the LLM, at no extra costs. The audio pipeline is very efficient; in the video, it's running on the cheapest Mac mini, 16GB of RAM (the one that sold out a couple of months back when people were buying them up for openclaws setups). Tutorials coming in the next few weeks: how to run your robot fully locally, or with your favorite agent. Stay tuned!

Andi Marafioti

56,158 görüntüleme • 4 ay önce

ROSClaw Integration Update Read the text in the video carefully ⊙ Robot is present both physically can in Nvidia Isaac Sim simulation. ⊙ The agent’s initial goal set to explore any object it finds in its spatial view. ⊙ Think of it as if the physical robot is seeing through the simulation. ⊙ ROSClaw vision uses Gemini Live for spatial narration ⊙ An object (a playground slide) is identified and the ROSClaw (OpenClaw) agent takes over. ⊙ The agent initiates action. Attempts to walk towards object. ⊙ None of it is pre-planned. The agent (robot) is acting on its own
0:44

ROSClaw Integration Update Read the text in the video carefully ⊙ Robot is present both physically can in Nvidia Isaac Sim simulation. ⊙ The agent’s initial goal set to explore any object it finds in its spatial view. ⊙ Think of it as if the physical robot is seeing through the simulation. ⊙ ROSClaw vision uses Gemini Live for spatial narration ⊙ An object (a playground slide) is identified and the ROSClaw (OpenClaw) agent takes over. ⊙ The agent initiates action. Attempts to walk towards object. ⊙ None of it is pre-planned. The agent (robot) is acting on its own

Irvin (in Japan 🇯🇵)

23,374 görüntüleme • 6 ay önce

Marco built Reachy Phone Home so Reachy Mini can detect when you’re on your phone, using Ultralytics YOLO26 vision, and respond in real time with voice + motion. Built on Arm (Apple Mac / Raspberry Pi 5) with Hugging Face 🤗 + Pollen Robotics 🦾, it’s now an award-winning project, earning an NVIDIA GTC Golden Ticket 🏆 It's great to see our developers build and win in the open AI ecosystem 👏
0:47

Marco built Reachy Phone Home so Reachy Mini can detect when you’re on your phone, using Ultralytics YOLO26 vision, and respond in real time with voice + motion. Built on Arm (Apple Mac / Raspberry Pi 5) with Hugging Face 🤗 + Pollen Robotics 🦾, it’s now an award-winning project, earning an NVIDIA GTC Golden Ticket 🏆 It's great to see our developers build and win in the open AI ecosystem 👏

Arm

26,936 görüntüleme • 6 ay önce

you don’t need a robot to see physical ai in action, you just need a browser. this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser. the result is the first ever demo of ACT that runs locally on the web. try it yourself now at
0:35

you don’t need a robot to see physical ai in action, you just need a browser. this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser. the result is the first ever demo of ACT that runs locally on the web. try it yourself now at

Binh

122,406 görüntüleme • 2 ay önce

This installation enables a live plant to control a machete. Plant machete has a control system that reads and utilizes the electrical noises found in a live philodendron. The system uses an open-source microcontroller connected to the plant to read varying resistance signals across the plant’s leaves. Using custom software, these signals are mapped in real-time to the movements of the joints of the industrial robot holding a machete. In this way, the movements of the machete are determined based on input from the plant. Essentially, the plant is the brain of the robot, controlling the machete, determining how it swings, jabs, slices, and interacts in space. Video Credit: David Bowen #engineering #technology
1:08

This installation enables a live plant to control a machete. Plant machete has a control system that reads and utilizes the electrical noises found in a live philodendron. The system uses an open-source microcontroller connected to the plant to read varying resistance signals across the plant’s leaves. Using custom software, these signals are mapped in real-time to the movements of the joints of the industrial robot holding a machete. In this way, the movements of the machete are determined based on input from the plant. Essentially, the plant is the brain of the robot, controlling the machete, determining how it swings, jabs, slices, and interacts in space. Video Credit: David Bowen #engineering #technology

Wevolver

48,348 görüntüleme • 4 ay önce

ELON MUSK: "Optimus will be the biggest product ever, but it is a very complex problem to solve. It's one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that, if you simply ask it to do something or show it a video, it can do the task without any programming. No one's ever achieved this, and there are many challenges in the design of the robot to achieve sufficient dexterity and to be very reliable and have long wear and tear, meaning it needs to be out in the field and not break down. Otherwise, you've got sort of a 70 kilogram robot that just flopped over and you've got to carry it out like a body, you know, it doesn't have wheels. And we don't want Optimus to go haywire. So it's a lot of work to scale, to get the design right and to scale production. This is going to be the hardest product to scale manufacturing that we've ever made at Tesla because everything on the robot is new. And the difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot. With Optimus, there is no supply chain. So we've had to build up a supply chain in its entirety or in-house the production. The Optimus production line that we're building out in Fremont in place of what used to be the Model S & X production, it looks incredible. It's quite stunning to see."
2:42

ELON MUSK: "Optimus will be the biggest product ever, but it is a very complex problem to solve. It's one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that, if you simply ask it to do something or show it a video, it can do the task without any programming. No one's ever achieved this, and there are many challenges in the design of the robot to achieve sufficient dexterity and to be very reliable and have long wear and tear, meaning it needs to be out in the field and not break down. Otherwise, you've got sort of a 70 kilogram robot that just flopped over and you've got to carry it out like a body, you know, it doesn't have wheels. And we don't want Optimus to go haywire. So it's a lot of work to scale, to get the design right and to scale production. This is going to be the hardest product to scale manufacturing that we've ever made at Tesla because everything on the robot is new. And the difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot. With Optimus, there is no supply chain. So we've had to build up a supply chain in its entirety or in-house the production. The Optimus production line that we're building out in Fremont in place of what used to be the Model S & X production, it looks incredible. It's quite stunning to see."

DogeDesigner

308,034 görüntüleme • 1 ay önce