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Robotics is getting its Raspberry Pi moment. This solves one of the biggest frictions in Physical AI: Everything has been fragmented. Different tools for control. Sensors. Calibration. Learning. Now it’s one unified stack: • motor control to VLA models in one system • built for real hardware, not just...

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Ilir Aliu

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Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,361 Aufrufe • vor 1 Monat

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.
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Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.

Dhruv Diddi

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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:
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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,804 Aufrufe • vor 5 Monaten

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.
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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.

Skild AI

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one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.
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one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 Aufrufe • vor 5 Monaten

A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.
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A few months ago we set out to understand exactly how our models can be used to accelerate progress in robotics and other AI systems that don’t just represent the world, but are actually able to interact with it. We’ve been collaborating with leading companies in the space and we’ve developed an initial approach that we're now opening up to accelerate development across the industry. Our fifth announcement is GWM Robotics. GWM Robotics is a learned simulator that generates synthetic data for scalable robot training and policy evaluation, removing the bottlenecks of physical hardware.

Runway

35,040 Aufrufe • vor 7 Monaten

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? 🤖👇
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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 Aufrufe • vor 3 Monaten

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:
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Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 Aufrufe • vor 8 Monaten

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

36,767 Aufrufe • vor 1 Monat

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.
1:00

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Neuracore

40,620 Aufrufe • vor 7 Monaten

The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.
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The next AI revolution is not just happening on screens. It is happening in the physical world. Robots, drones, autonomous vehicles, and smart machines all need one thing before they can operate safely: Precise location. Traditional GPS can be off by several meters. For humans, that may be fine. For autonomous machines, it can be dangerous. GEODNET is building the spatial intelligence layer for Physical AI, using RTK positioning to improve accuracy from meters to within one centimeter. Over 20,000 base stations. 170+ countries. 11,000+ cities. 130+ enterprise customers. Physical AI needs real-world infrastructure. That infrastructure is being built now.

GEODNET

137,630 Aufrufe • vor 22 Tagen

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:
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Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

21,319 Aufrufe • vor 3 Monaten

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
2:32

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 Aufrufe • vor 19 Tagen

2. NVIDIA is building the GPT of humanoid robots. They just launched Isaac GR00T N1.5 - a foundation model for general purpose robotics. Here’s how it works: → A human demos the task once → Cosmos (their physics AI model) generates 1,000s of variations → Omniverse simulates the motions in high fidelity → The robot trains entirely in simulation → Then fine-tunes itself in the real world Robots can now learn general skills across tasks, tools, even body types with just one human demo. AI isn’t just thinking in text anymore. It’s perceiving. Reasoning. Moving. Physical AI is here and it’s training itself.
2:21

2. NVIDIA is building the GPT of humanoid robots. They just launched Isaac GR00T N1.5 - a foundation model for general purpose robotics. Here’s how it works: → A human demos the task once → Cosmos (their physics AI model) generates 1,000s of variations → Omniverse simulates the motions in high fidelity → The robot trains entirely in simulation → Then fine-tunes itself in the real world Robots can now learn general skills across tasks, tools, even body types with just one human demo. AI isn’t just thinking in text anymore. It’s perceiving. Reasoning. Moving. Physical AI is here and it’s training itself.

Shruti

70,359 Aufrufe • vor 1 Jahr

SOMEONE JUST BUILT AN OPERATING SYSTEM FOR ROBOTS not a framework... not a library an actual OS dimos is an open-source platform that lets you build, run, and deploy software on any robot humanoids, quadrupeds, drones, wheeled bots you write python modules .... they talk to each other via typed streams...just like microservices, but for physical hardware the wild part..? you can give your AI editor (cursor, claude code) MCP access to send real physical commands "move forward 1 meter" → robot moves. it ships with a full navigation stack SLAM, collision avoidance, route planning. plus a dashboard to debug, visualize, and control your robot in real-time via waypoints, VR, or keyboard. supported hardware already includes unitree G1 humanoids, dji drones, and multiple robot arms. programming physical robots is finally as simple as programming software
3:35

SOMEONE JUST BUILT AN OPERATING SYSTEM FOR ROBOTS not a framework... not a library an actual OS dimos is an open-source platform that lets you build, run, and deploy software on any robot humanoids, quadrupeds, drones, wheeled bots you write python modules .... they talk to each other via typed streams...just like microservices, but for physical hardware the wild part..? you can give your AI editor (cursor, claude code) MCP access to send real physical commands "move forward 1 meter" → robot moves. it ships with a full navigation stack SLAM, collision avoidance, route planning. plus a dashboard to debug, visualize, and control your robot in real-time via waypoints, VR, or keyboard. supported hardware already includes unitree G1 humanoids, dji drones, and multiple robot arms. programming physical robots is finally as simple as programming software

Vaishnavi

19,642 Aufrufe • vor 2 Monaten

One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.
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One of the why questions we’re always asking is how can we help operations employees access inventory more efficiently? We’re taking a big leap forward by introducing DeepFleet, an exciting new AI model that makes our one million+ robot fleet work smarter. Think of DeepFleet as an intelligent traffic management system for our fulfillment centers. Just like smart traffic systems reduce congestion in cities, DeepFleet coordinates our robots’ movements to find optimal paths and reduce bottlenecks. By leveraging our deep expertise in robotics and combining it with AWS tools like SageMaker, we’ve built an AI model that learns from our extensive data on inventory movement to make real-time decisions that improve robot travel time by 10% (which at our scale is pretty significant). What makes this particularly exciting is how AI and robotics create value across the board. For customers, it means faster delivery times and lower costs. For employees, it opens up new opportunities – our robots handle physically demanding tasks and heavy lifting, making our fulfillment centers safer, while our people take on more technical roles. This combination of robotics and generative AI is just getting started, and am looking forward to what we’ll invent next.

Andy Jassy

42,327 Aufrufe • vor 1 Jahr

We are back again :) After three weeks of quiet building. Introducing Genesis World 1.0, our latest simulation platform, the second release in our full-stack suite. Open-sourced. Robotics is still bottlenecked by the 1× speed of the physical world. Every model, checkpoint, and data recipe eventually needs to be tested on physical hardware, slowly, expensively, and with limited coverage. One hour in reality can become 100 days in simulation. That is how robotics model iteration moves from a wall-clock bottleneck to a compute problem. To make this work, simulation has to be both fast and trustworthy. Over the past year, we rebuilt the entire stack: a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, and a photo-realistic renderer purpose-built for physical AI applications. We built Nyx, a high-performance path-traced rendering engine for robotics application. Genesis World 1.0 achieves near realtime performance with our latest development for penetration-free IPC solver, supporting various types of deformables beyond rigid bodies. It supports contact-rich, dexterous manipulation simulation across different embodiments: unitree, sharpa, wuji, genesis hand and various types of grippers. Under the hood is Quadrants, our effort in pushing forward cross-platform GPU-accelerated computation. Quadrants started as a fork of Taichi, and we rebuilt most of the critical parts for optimizing simulation workloads, giving 10x faster launch time and up to 4.6x runtime performance compared to the initial Genesis release. Together, they bring us to an unprecedentedly low sim-to-real gap, enabling zero-shot real-to-sim model evaluation and much faster iteration of GENE. All available today. Genesis World 1.0: Quadrants: Nyx:
2:51

We are back again :) After three weeks of quiet building. Introducing Genesis World 1.0, our latest simulation platform, the second release in our full-stack suite. Open-sourced. Robotics is still bottlenecked by the 1× speed of the physical world. Every model, checkpoint, and data recipe eventually needs to be tested on physical hardware, slowly, expensively, and with limited coverage. One hour in reality can become 100 days in simulation. That is how robotics model iteration moves from a wall-clock bottleneck to a compute problem. To make this work, simulation has to be both fast and trustworthy. Over the past year, we rebuilt the entire stack: a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, and a photo-realistic renderer purpose-built for physical AI applications. We built Nyx, a high-performance path-traced rendering engine for robotics application. Genesis World 1.0 achieves near realtime performance with our latest development for penetration-free IPC solver, supporting various types of deformables beyond rigid bodies. It supports contact-rich, dexterous manipulation simulation across different embodiments: unitree, sharpa, wuji, genesis hand and various types of grippers. Under the hood is Quadrants, our effort in pushing forward cross-platform GPU-accelerated computation. Quadrants started as a fork of Taichi, and we rebuilt most of the critical parts for optimizing simulation workloads, giving 10x faster launch time and up to 4.6x runtime performance compared to the initial Genesis release. Together, they bring us to an unprecedentedly low sim-to-real gap, enabling zero-shot real-to-sim model evaluation and much faster iteration of GENE. All available today. Genesis World 1.0: Quadrants: Nyx:

Genesis AI

305,139 Aufrufe • vor 1 Monat

Physical Intelligence (Physical Intelligence) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection. In this episode of the Lightcone Podcast , co-founder Quan Vuong (Quan Vuong) sat down with Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies. 00:00 — Robotics just got cheaper 00:41 — The GPT moment for robotics 02:24 — Why robots didn’t work before 05:30 — The breakthrough that changed everything 09:12 — The data problem 13:33 — Robots learning without data 15:05 — Robots folding laundry (for real) 22:18 — From engineering problem → ops problem 29:12 — The startup playbook 38:46 — Thousands of robotics startups are coming
49:26

Physical Intelligence (Physical Intelligence) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection. In this episode of the Lightcone Podcast , co-founder Quan Vuong (Quan Vuong) sat down with Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies. 00:00 — Robotics just got cheaper 00:41 — The GPT moment for robotics 02:24 — Why robots didn’t work before 05:30 — The breakthrough that changed everything 09:12 — The data problem 13:33 — Robots learning without data 15:05 — Robots folding laundry (for real) 22:18 — From engineering problem → ops problem 29:12 — The startup playbook 38:46 — Thousands of robotics startups are coming

Y Combinator

115,590 Aufrufe • vor 2 Monaten