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Every robot should see, understand, and explore the physical world. Introducing LightNav-0: one generalist navigation brain by Light Origins. Any robot. Any task. Anywhere. Open source. Out of the box. Coming soon.

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LightOrigins

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58,370 görüntüleme • 1 ay önce •via X (Twitter)

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Generalist robot policies need a benchmark that works across any robot and any policy. 🦾 Introducing RoboLab, a high‑fidelity simulation benchmark built on NVIDIA Isaac and Omniverse to evaluate generalist robot policies in diverse, photoreal, physics‑based environments. Coming soon to the NVIDIA Isaac Lab‑Arena roadmap for large‑scale, robotic policy evaluation. 📖 #NationalRoboticsWeek
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Generalist robot policies need a benchmark that works across any robot and any policy. 🦾 Introducing RoboLab, a high‑fidelity simulation benchmark built on NVIDIA Isaac and Omniverse to evaluate generalist robot policies in diverse, photoreal, physics‑based environments. Coming soon to the NVIDIA Isaac Lab‑Arena roadmap for large‑scale, robotic policy evaluation. 📖 #NationalRoboticsWeek

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Super excited about Hydra-0 from Hongyu Li and team! The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.

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🏆 Cosmos 3 just topped 7 physical AI leaderboards. NVIDIA Cosmos™ 3, the open omni-model for physical AI, ranks #1 across world generation, robot action policy, and industrial vision understanding. 🌎 World generation: Artificial Analysis, PAI-Bench, Physics-IQ, R-Bench 🤖 Robot policy: RoboLab 👁️ Vision: VANTAGE-Bench, TAR One model. Every domain of physical AI. Available now on Hugging Face ▶️

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1/ NVIDIA just open-sourced Cosmos 3 at GTC Taipei! It's the first fully open "omnimodel" for physical AI - one model that understands the real world, predicts what happens next, and generates the actions a robot should take. Weights, code, datasets. All open. And this is really big. Lets dig into everything: 🧵

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Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

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ROBOTS RUN MARATHONS… AND MEMES At the World Humanoid Robot Games, most bots were out there sprinting like Olympians. But then one “chilling meme robot” showed up just to vibe. Instead of chasing medals, it waddled along with pure chill energy. No stress, no pressure, just vibes. Honestly, maybe humans should take notes. Not everything has to be a race. Source: reborn_agi

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Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

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I didn't get what GPT-6 meant for robotics until I actually tried it. GPT-6 Astra just does physical ICL out of the box. we drop a recording of a human doing a novel task into 𝗰𝗼𝗱𝗲𝘅 app. Prompt it to drive a robot arm the same way. It just works on the first pass!
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I didn't get what GPT-6 meant for robotics until I actually tried it. GPT-6 Astra just does physical ICL out of the box. we drop a recording of a human doing a novel task into 𝗰𝗼𝗱𝗲𝘅 app. Prompt it to drive a robot arm the same way. It just works on the first pass!

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MAKING AXIS TASKS FEEL EASY 👀🤖 I made a quick video showing how to complete an Axis Robotics task step by step. The task gives you a clear goal, steps, controls and camera views to guide you. In this one, the goal is simple, Open the trash bin, pick up the onion, and place it inside. 🧅 Just follow the instructions, control the robot arm and complete the task. What I like is that even beginners can understand what they need to do. But the interesting part is what happens with these interactions after we complete them. Each completed task can contribute valuable robot training data, helping Physical Ai systems learn how to handle different objects and environments. Small tasks from the community can become useful data for teaching robots how to interact with the real world. That’s what makes Axis interesting to me.

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