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Excited to share LWD: Learning While Deploying. Our robots learn while doing real tasks—restocking groceries, brewing Gongfu tea, making cocktails, making juice, and packing shoes. Deployment is no longer just evaluation; it becomes the training loop. 🧵

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Jianlan Luo

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621,657 görüntüleme • 1 ay önce •via X (Twitter)

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Our CTO boyuan is demoing our new localization algorithm, which surpasses leading industry solutions. Localization enables a robot to determine its position in an environment, which is essential for navigation. While most systems require robots to start from a predefined location, ours doesn’t. It lets robots boot up anywhere and immediately locate themselves, making deployment far more flexible for real world scenarios.
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Our CTO boyuan is demoing our new localization algorithm, which surpasses leading industry solutions. Localization enables a robot to determine its position in an environment, which is essential for navigation. While most systems require robots to start from a predefined location, ours doesn’t. It lets robots boot up anywhere and immediately locate themselves, making deployment far more flexible for real world scenarios.

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Choice menus double the workload for teachers, while making it less likely that students will learn what they need to learn. Students need us to decide on the best learning activities. ##education##teachersoftiktok##learning##achievementscience

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The robot is learning several novel tasks instantly, after just ONE demonstration each... Instant Policy makes it possible: no extra training, no weight updates, just pure in-context learning. It just got accepted at ICLR 2025, and it’s changing how robots learn. With just a single demo, a robot can pick up a new task and start performing it right away. Why this is a big deal: ✅ Learns tasks instantly with just one or a few demonstrations ✅ Improves over time as more demonstrations are given ✅ Uses simulation-based training with “pseudo-demonstrations” for scalability ✅ Can transfer skills across different robots and even follow language-defined tasks It brings in-context learning to robotics, opening up new possibilities for flexible, real-world automation. You can try it yourself: code and weights are available at • • • • Thank you to Edward Johns, Director of the Robot Learning Lab at Imperial College for sharing their work! 🙏

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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)
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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

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While Unitree sells a lot of dumb humanoid robots to developers and researchers, they're also working on their own AI models. Robots making robots: Unitree’s UnifoLM-X1-0 AI model in action, performing assembly tasks in Unitree's own factory.
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Our elites have failed. They're profiting from chaos while public services collapse. I just dropped a new episode with William Clouston SDP diving into the "Doom Loop" of government waste, diversity quotas, and the grift that's making everyone poorer. Links to full episode in 🧵
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