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How can robots reliably place objects in diverse real-world tasks? 🤖🔍 Placement is tough—objects vary in shape and placement modes (such as stacking, hanging, and insertion), making it a challenging problem. We introduce AnyPlace, a two-stage method trained purely on synthetic data to predict diverse placement poses of unseen... show more

Animesh Garg

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25,378 次观看 • 1 年前 •via X (Twitter)

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🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

LeRobot

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You can actually interact with the world simulator directly in the browser. 🤖 Here is a quick screen recording (8x speed) of me playing with it: real-time action-conditioned video prediction across rigid objects, deformable objects, rope, and object piles. Try it yourself (no install required): Huge kudos to my student Yixuan Wang for making the interactive demo happen!

You can actually interact with the world simulator directly in the browser. 🤖 Here is a quick screen recording (8x speed) of me playing with it: real-time action-conditioned video prediction across rigid objects, deformable objects, rope, and object piles. Try it yourself (no install required): Huge kudos to my student Yixuan Wang for making the interactive demo happen!

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We’ve seen humanoid robots walk around for a while, but when will they actually help with useful tasks in daily life? The challenge here is the diversity and complexity of real-world scenes. Our new work tackles this problem via 3D visuomotor policy learning. Using data from only 1 scene, our Improved 3D Diffusion Policy (iDP3) enables a full-sized humanoid robot to autonomously pick&place objects, pour water, and wipe tables, in the wild open world. (and all these skills are useful, right?) Web: Fully open-sourced code:

We’ve seen humanoid robots walk around for a while, but when will they actually help with useful tasks in daily life? The challenge here is the diversity and complexity of real-world scenes. Our new work tackles this problem via 3D visuomotor policy learning. Using data from only 1 scene, our Improved 3D Diffusion Policy (iDP3) enables a full-sized humanoid robot to autonomously pick&place objects, pour water, and wipe tables, in the wild open world. (and all these skills are useful, right?) Web: Fully open-sourced code:

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75,248 次观看 • 1 年前

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Jonathan Stephens

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Ilir Aliu - eu/acc

10,241 次观看 • 1 年前

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56,151 次观看 • 7 个月前

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 “𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜" - the idea that we can simulate real-world environments so well that robots trained in simulation will work perfectly in reality. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲: Train in virtual worlds → deploy anywhere. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: I’ve seen too many teams fall into this trap. After working with manipulation teams at Berkeley, Imperial, and Dyson, here’s the pattern: • 𝗪𝗲𝗲𝗸 𝟭: “Our policy works perfectly in simulation!” • 𝗪𝗲𝗲𝗸 𝟰: “Why doesn’t this work on real objects?” • 𝗠𝗼𝗻𝘁𝗵 𝟮: “We basically need to retrain from scratch with real data.” 𝗧𝗵𝗲 𝗴𝗮𝗽 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻’𝘁 𝗯𝗿𝗶𝗱𝗴𝗲: Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽𝘀. • Real friction vs simulated surface textures • Manufacturing tolerances vs perfect CAD models • Dynamic lighting vs controlled virtual environments • Sensor noise vs instantaneous virtual readings 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻'𝘁 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁: Building these detailed simulated environments takes forever. If it takes 7 days to build a simulated kitchen in simulation, wouldn't it be better to just collect real-world data in a real kitchen instead? 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗺𝗲 𝘄𝗿𝗼𝗻𝗴 - simulation is incredible for debugging, safety testing, and exploring edge cases. But it's not a magic solution to real-world deployment. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically while making real-world data collection as efficient and flexible as possible. This is why Neuracore focuses on streamlined real-world data infrastructure. Because no amount of virtual training can replace understanding how your robot actually behaves in actual environments. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗰𝗮𝗻'𝘁 𝗯𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘄𝗮𝘆. What’s been your experience with sim-to-real transfer?

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 “𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜" - the idea that we can simulate real-world environments so well that robots trained in simulation will work perfectly in reality. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲: Train in virtual worlds → deploy anywhere. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: I’ve seen too many teams fall into this trap. After working with manipulation teams at Berkeley, Imperial, and Dyson, here’s the pattern: • 𝗪𝗲𝗲𝗸 𝟭: “Our policy works perfectly in simulation!” • 𝗪𝗲𝗲𝗸 𝟰: “Why doesn’t this work on real objects?” • 𝗠𝗼𝗻𝘁𝗵 𝟮: “We basically need to retrain from scratch with real data.” 𝗧𝗵𝗲 𝗴𝗮𝗽 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻’𝘁 𝗯𝗿𝗶𝗱𝗴𝗲: Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽𝘀. • Real friction vs simulated surface textures • Manufacturing tolerances vs perfect CAD models • Dynamic lighting vs controlled virtual environments • Sensor noise vs instantaneous virtual readings 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻'𝘁 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁: Building these detailed simulated environments takes forever. If it takes 7 days to build a simulated kitchen in simulation, wouldn't it be better to just collect real-world data in a real kitchen instead? 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗺𝗲 𝘄𝗿𝗼𝗻𝗴 - simulation is incredible for debugging, safety testing, and exploring edge cases. But it's not a magic solution to real-world deployment. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically while making real-world data collection as efficient and flexible as possible. This is why Neuracore focuses on streamlined real-world data infrastructure. Because no amount of virtual training can replace understanding how your robot actually behaves in actual environments. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗰𝗮𝗻'𝘁 𝗯𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘄𝗮𝘆. What’s been your experience with sim-to-real transfer?

Stephen James

25,300 次观看 • 9 个月前

Introducing Attio Objects 🚀 We know how hard it is to find a CRM that fits your unique business model. That's why we built Attio Objects – our powerful data model with custom objects that gives you complete flexibility to structure your CRM exactly how you need it. Along with custom objects, we've also introduced new standard objects: - Workspaces and Users objects for PLG businesses. - A robust Deals object for sales-driven companies. This is the culmination of a 4-year effort, with 3 years of work put in even before launching Attio. Since day one, we've been determined to solve the fundamental problem in the CRM space: the trade-off between power and time-to-value. If you wanted power and flexibility, your CRM would take forever to build and not work well with your stack. If you wanted speed, you'd need to use highly opinionated, inflexible software that doesn't really work for your business. That ends today. With Attio, you no longer have to compromise. Build your CRM your way, fast. Iterate as you grow. High-growth startups like Replicate, , and Modal and more are already using Attio's object architecture to perfectly match their businesses and accelerate their growth. To get all the details, check out our blog post 👇

Introducing Attio Objects 🚀 We know how hard it is to find a CRM that fits your unique business model. That's why we built Attio Objects – our powerful data model with custom objects that gives you complete flexibility to structure your CRM exactly how you need it. Along with custom objects, we've also introduced new standard objects: - Workspaces and Users objects for PLG businesses. - A robust Deals object for sales-driven companies. This is the culmination of a 4-year effort, with 3 years of work put in even before launching Attio. Since day one, we've been determined to solve the fundamental problem in the CRM space: the trade-off between power and time-to-value. If you wanted power and flexibility, your CRM would take forever to build and not work well with your stack. If you wanted speed, you'd need to use highly opinionated, inflexible software that doesn't really work for your business. That ends today. With Attio, you no longer have to compromise. Build your CRM your way, fast. Iterate as you grow. High-growth startups like Replicate, , and Modal and more are already using Attio's object architecture to perfectly match their businesses and accelerate their growth. To get all the details, check out our blog post 👇

Attio

26,821 次观看 • 2 年前

As announced in partnership with NVIDIA at CES, we’re excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)

As announced in partnership with NVIDIA at CES, we’re excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)

Stability AI

181,441 次观看 • 1 年前

Massive performance improvement. This is a bit more of technical post, but man do I love this stuff! Units navigate the map using a 'Navigation Mesh'. Before, I was using one giant nav mesh that spanned the entire map. The more objects that were placed (especially on a large map such as this 'RadarAttack' map designed by Syphotic | Steel Command), the larger the 'lag' would be after placement. You can see here that there is a massive frame drop and the navmesh doesnt update for almost 5 seconds. Now, there are a ton of tiny navmeshes that connect to one another, and together they cover the whole map. Now, when an object is placed, the navmesh will update instantly, because it no longer needs to parse through every object on the map (potentially thousands!!!). It only needs to parse through the objects that exist in the mini navmesh that the object was placed in (probably only 1-5 objects now!). Performance XP Boost +100! Charles Horwood You might appreciate this one :) #Rts #RTSGame #IndieGame

Massive performance improvement. This is a bit more of technical post, but man do I love this stuff! Units navigate the map using a 'Navigation Mesh'. Before, I was using one giant nav mesh that spanned the entire map. The more objects that were placed (especially on a large map such as this 'RadarAttack' map designed by Syphotic | Steel Command), the larger the 'lag' would be after placement. You can see here that there is a massive frame drop and the navmesh doesnt update for almost 5 seconds. Now, there are a ton of tiny navmeshes that connect to one another, and together they cover the whole map. Now, when an object is placed, the navmesh will update instantly, because it no longer needs to parse through every object on the map (potentially thousands!!!). It only needs to parse through the objects that exist in the mini navmesh that the object was placed in (probably only 1-5 objects now!). Performance XP Boost +100! Charles Horwood You might appreciate this one :) #Rts #RTSGame #IndieGame

Smitty | Steel Command

60,072 次观看 • 6 个月前