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At OpenMind, we’re building an open-source AI operating system that works across any robot. With OpenMind’s Brainpack + @NVIDIA Jetson Thor, robots can run OM1 software and gain full autonomy, even if they weren’t designed for it.

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OpenMind

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68,931 Aufrufe • vor 8 Monaten •via X (Twitter)

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Powered by NVIDIA Jetson Thor, the OpenMind BrainPack brings next-gen autonomy to humanoids and quadrupeds. Thor gives OM1 the compute to run cutting-edge AI models, navigate real-world urban environments, and uphold spectator privacy. As robots gain new capabilities, Thor ensures they run smoothly, safely, and intelligently.
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Powered by NVIDIA Jetson Thor, the OpenMind BrainPack brings next-gen autonomy to humanoids and quadrupeds. Thor gives OM1 the compute to run cutting-edge AI models, navigate real-world urban environments, and uphold spectator privacy. As robots gain new capabilities, Thor ensures they run smoothly, safely, and intelligently.

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We gave our AI system a spec, and in under 2 weeks, it designed, verified, and deployed a chip that beats NVIDIA. It’s built for low-power physical AI workloads. We’re running live inference on >B+ parameter models like Llama, Qwen, and Kimi, serving at 3.4x better perf/watt than NVIDIA Jetson. From just a specification, our AI system autonomously generated all of the RTL Design, UVM verification, formal proofs, firmware, drivers, and kernels, co-designing the model, software and silicon as one optimization loop. Better AI can now design better chips to run AI, leading to a loop of recursive-self improvement towards our path to abundant intelligence.
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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:
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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

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CES report #30. Walking around with a guy who partially built DOS (Jim Harding) and now is building a new kind of operating system that orchestrates robots and humans working together. The Holodeck needs a hyper smart network. I wrote an important book on contextual software years ago. A new operating system is coming. It doesn’t run one computer at a time. It runs us and all computers at the same time. It will show us how to connect with autonomous vehicles, get served by robots, and much more. And can communicate with competitive ones that Elon Musk and others are building. The future: This is the biggest idea I have ever heard.
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CES report #30. Walking around with a guy who partially built DOS (Jim Harding) and now is building a new kind of operating system that orchestrates robots and humans working together. The Holodeck needs a hyper smart network. I wrote an important book on contextual software years ago. A new operating system is coming. It doesn’t run one computer at a time. It runs us and all computers at the same time. It will show us how to connect with autonomous vehicles, get served by robots, and much more. And can communicate with competitive ones that Elon Musk and others are building. The future: This is the biggest idea I have ever heard.

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What if one AI brain could run every robot on the planet—from a humanoid to a warehouse arm—all at once? 🧠 , CEO and Co-Founder, and Abhinav Gupta, President and Co-Founder of Skild AI, explain how they are building "OmniBrain," a universal foundation model designed to generalize intelligence across any robot form factor and task. 📺 Watch the episode:

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Robotics: coding agents’ next frontier. So how good are they? We introduce CaP-X: an open-source framework and benchmark for coding agents, where they write code for robot perception and control, execute it on sim and real robots, observe the outcomes, and iteratively improve code reliability. From NVIDIA Berkeley AI Research CMU Robotics Institute Stanford AI Lab 🧵
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Robotics: coding agents’ next frontier. So how good are they? We introduce CaP-X: an open-source framework and benchmark for coding agents, where they write code for robot perception and control, execute it on sim and real robots, observe the outcomes, and iteratively improve code reliability. From NVIDIA Berkeley AI Research CMU Robotics Institute Stanford AI Lab 🧵

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NEWS: Nvidia has announced that they are partnering with General Motors on full self-driving. Nvidia CEO Jensen Huang: “Today, I’m super excited to announce that GM has selected Nvidia to partner with to build their future self-driving car fleet. We’re looking forward to building with GM on AI for manufacturing so they can revolutionize the way they manufacture, AI for enterprise so they can revolutionize the way they work, design and simulate cars, and also AI for in the car.”

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Robots are maturing. AI models are advancing. Deployments are rarely scaling. Today, deploying a robot still means stitching together systems that were never designed to work together. For every robot. Every time. The industry needs a unified intelligence infrastructure that any robot, any model and any workflow can plug into. We call this an intelligence grid. Infrastructure that makes intelligence composable, deployable, and continuously adapting. Every connected robot evolves as the platform advances. More on what we're building:
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