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New open-source library speeds up whole-body imitation learning. Train the policy below in under 20 minutes ⬇️ LocoMuJoCo2 brings biomechanics and humanoid robotics into one clean, high-speed benchmark. Built for researchers who want to train smarter and faster. What it offers ✅ 22K+ motion trajectories per humanoid model ✅...

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Ilir Aliu

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23,160 görüntüleme • 1 yıl önce •via X (Twitter)

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🚀 ASAP is now FULLY open-source! 🚀 ✅ Humanoid RL motion tracking & delta actions ✅ Motion retargeting to any humanoid ✅ ASAP Benchmark motions + pretrained policies ✅ Sim2Sim & Sim2Real ready — run ASAP in sim or on your G1 robot! 🔗 🎥👇
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🚀 ASAP is now FULLY open-source! 🚀 ✅ Humanoid RL motion tracking & delta actions ✅ Motion retargeting to any humanoid ✅ ASAP Benchmark motions + pretrained policies ✅ Sim2Sim & Sim2Real ready — run ASAP in sim or on your G1 robot! 🔗 🎥👇

Tairan He

39,412 görüntüleme • 11 ay önce

NVIDIA DROPPED A MOTION DIFFUSION MODEL FOR HUMANOID ROBOTS trained on 700 hours of mocap data kimodo generates high-quality 3D human and robot motions from text prompts you control it with: → full-body pose keyframes → end-effector positions/rotations → 2D paths and waypoints works on human skeletons and unitree G1 robot plug the outputs directly into mujoco or retarget to other robots using GMR has a web-based interactive demo with a timeline editor. runs locally needs ~17GB VRAM to run inference open source under apache 2.0
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NVIDIA DROPPED A MOTION DIFFUSION MODEL FOR HUMANOID ROBOTS trained on 700 hours of mocap data kimodo generates high-quality 3D human and robot motions from text prompts you control it with: → full-body pose keyframes → end-effector positions/rotations → 2D paths and waypoints works on human skeletons and unitree G1 robot plug the outputs directly into mujoco or retarget to other robots using GMR has a web-based interactive demo with a timeline editor. runs locally needs ~17GB VRAM to run inference open source under apache 2.0

Vaishnavi

17,520 görüntüleme • 2 ay önce

#NVIDIAIsaac Sim 5.0 and Isaac Lab 2.2 are now available in early developer preview on Github. 🎉 These releases give #Robotics developers early access to cutting-edge tools to simulate, train, and validate robots in a physics-based simulation environment. What’s new? ✅Open-source ✅Extensions for synthetic data generation ✅Robot models Read the tech blog to learn more ➡️ #GTCParis #VivaTech
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#NVIDIAIsaac Sim 5.0 and Isaac Lab 2.2 are now available in early developer preview on Github. 🎉 These releases give #Robotics developers early access to cutting-edge tools to simulate, train, and validate robots in a physics-based simulation environment. What’s new? ✅Open-source ✅Extensions for synthetic data generation ✅Robot models Read the tech blog to learn more ➡️ #GTCParis #VivaTech

NVIDIA Robotics

13,613 görüntüleme • 1 yıl önce

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:
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Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 görüntüleme • 7 ay önce

Just 1 piece of string and 20 minutes a day-play with your child, move together, and build both body and brain✅ The more they play, the smarter and more agile they become✌️😎
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Just 1 piece of string and 20 minutes a day-play with your child, move together, and build both body and brain✅ The more they play, the smarter and more agile they become✌️😎

Wellness Xplorer

56,051 görüntüleme • 6 ay önce

NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:
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NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:

NVIDIA Robotics

166,289 görüntüleme • 1 ay önce

Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are. Now it works from raw images in seconds. roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. No special hardware. No custom setup. You toggle between robot descriptions and the system figures out the rest. The idea is simple: ✅ Hand eye calibration from plain RGB or RGB D images ✅ Only three robot poses needed for millimeter accuracy ✅ Works with any ROS 2 compatible robot and camera ✅ Fully open source under Apache 2.0 It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second. If you want to try it: roboreg: ROS 2 roboreg: Hydra paper: pip install roboreg More details and discussion on Open Robotics Discourse:
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Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are. Now it works from raw images in seconds. roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. No special hardware. No custom setup. You toggle between robot descriptions and the system figures out the rest. The idea is simple: ✅ Hand eye calibration from plain RGB or RGB D images ✅ Only three robot poses needed for millimeter accuracy ✅ Works with any ROS 2 compatible robot and camera ✅ Fully open source under Apache 2.0 It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second. If you want to try it: roboreg: ROS 2 roboreg: Hydra paper: pip install roboreg More details and discussion on Open Robotics Discourse:

Ilir Aliu

18,406 görüntüleme • 7 ay önce

Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is hard for deep RL. But by learning constraint relationships and storing them in memory, robots can solve these tasks much faster; with fewer trials and less training. Why it works ✅ Learns symbolic rules about action constraints ✅ Uses memory to transfer what it learned across tasks ✅ Handles real-world exploration with just 30 minutes of data ✅ Needs 10x fewer episodes than deep RL approaches This memory-based method shows a promising path forward for robots learning structured, real-world tasks. Full video: Paper: Thank you, Mrinal Verghese for sharing this amazing work! 🙏
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Robots struggle with strict action rules…memory and symbols help them learn fast. [Project + Full video link ⬇️] Robots struggle when tasks require specific steps in a fixed order. What if memory helped them think symbolically and learn faster? Solving tasks like unlocking a door then opening it is hard for deep RL. But by learning constraint relationships and storing them in memory, robots can solve these tasks much faster; with fewer trials and less training. Why it works ✅ Learns symbolic rules about action constraints ✅ Uses memory to transfer what it learned across tasks ✅ Handles real-world exploration with just 30 minutes of data ✅ Needs 10x fewer episodes than deep RL approaches This memory-based method shows a promising path forward for robots learning structured, real-world tasks. Full video: Paper: Thank you, Mrinal Verghese for sharing this amazing work! 🙏

Ilir Aliu - eu/acc

10,241 görüntüleme • 1 yıl önce

NEWS: Phantom Blade 0 🚀 Latest details😎 ✅Main story: 30 hours of playtime ✅30-35 hours for side content ✅30 main and 20 secondary weapons ✅Respawn in the same boss phase ✅If you lose in phase 2, you respawn in it ✅4 difficulty tiers ✅Repeat boss challenge mode ✅Budget: ~55 million ✅Dev team of 100+ ✅Release date to be revealed this year ✅Projected to launch in 2026 Play or pass?🎮 Source: Tieba (Chinese) Phantom Blade Zero
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NEWS: Phantom Blade 0 🚀 Latest details😎 ✅Main story: 30 hours of playtime ✅30-35 hours for side content ✅30 main and 20 secondary weapons ✅Respawn in the same boss phase ✅If you lose in phase 2, you respawn in it ✅4 difficulty tiers ✅Repeat boss challenge mode ✅Budget: ~55 million ✅Dev team of 100+ ✅Release date to be revealed this year ✅Projected to launch in 2026 Play or pass?🎮 Source: Tieba (Chinese) Phantom Blade Zero

Rino🚀

50,206 görüntüleme • 1 yıl önce

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!
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🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

Zubair Irshad

20,366 görüntüleme • 1 yıl önce

Delivering for You. That’s what Saskatchewan’s 2025-26 Budget does. Balanced, responsible, and focused on what matters most to Saskatchewan people. ✅A $12 million surplus – keeping us in a strong fiscal position to respond to challenges like tariffs ✅Over $250 million in new tax savings – helping you and your family with the cost of living ✅Record $8.1 billion for health care – better, faster care when you need it ✅More support for students, teachers, and growing classrooms ✅The largest infrastructure investment in Saskatchewan history ✅Safer communities and stronger local services This is a budget that protects today and prepares for tomorrow. We’re delivering the priorities of Saskatchewan residents, and we’re standing up for Saskatchewan. Visit: to see how the Budget delivers for you.
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Delivering for You. That’s what Saskatchewan’s 2025-26 Budget does. Balanced, responsible, and focused on what matters most to Saskatchewan people. ✅A $12 million surplus – keeping us in a strong fiscal position to respond to challenges like tariffs ✅Over $250 million in new tax savings – helping you and your family with the cost of living ✅Record $8.1 billion for health care – better, faster care when you need it ✅More support for students, teachers, and growing classrooms ✅The largest infrastructure investment in Saskatchewan history ✅Safer communities and stronger local services This is a budget that protects today and prepares for tomorrow. We’re delivering the priorities of Saskatchewan residents, and we’re standing up for Saskatchewan. Visit: to see how the Budget delivers for you.

Scott Moe

18,901 görüntüleme • 1 yıl önce

Build photoreal digital humans - in minutes. ✅ Rigged & motion-ready ✅ Lip sync + emotional nuance ✅ Voice-cloned, drag-and-drop editing ✅ Instant export to socials, engines, or studios From interactive ambassadors to learning companions - AIGE by Antix makes characters that feel human. Join Beta waitlist: #AIGE #VirtualHumans #CreatorTech #Antix #AIIdentity
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Build photoreal digital humans - in minutes. ✅ Rigged & motion-ready ✅ Lip sync + emotional nuance ✅ Voice-cloned, drag-and-drop editing ✅ Instant export to socials, engines, or studios From interactive ambassadors to learning companions - AIGE by Antix makes characters that feel human. Join Beta waitlist: #AIGE #VirtualHumans #CreatorTech #Antix #AIIdentity

Antix.in

117,132 görüntüleme • 10 ay önce

REVERSE HANG POWER CLEAN 🔄 If you want to be explosive, add this OLY variation to your routine! Want to turn it up a notch? Add a BOSU Ball to improve balance & stability. ✅ Speed ✅ Power ✅ Balance ✅ Stability ✅ Improves Sprint Technique
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REVERSE HANG POWER CLEAN 🔄 If you want to be explosive, add this OLY variation to your routine! Want to turn it up a notch? Add a BOSU Ball to improve balance & stability. ✅ Speed ✅ Power ✅ Balance ✅ Stability ✅ Improves Sprint Technique

Tim Cortazzo

28,290 görüntüleme • 1 yıl önce

Most imitation learning policies break when the camera moves or the robot changes. NOT THIS ONE 👇 [📍 Bookmark for later ] A new 3D scene representation encoder, tackles this by enabling zero-shot generalization to unseen embodiments and viewpoints… And it works with any IL algorithm. The trick? •Use a 2D foundation model to extract semantic features •Lift them into 3D space for localization (not semantics) •Condition the IL policy on this spatially grounded vector Across 93 simulated and 6 real tasks, Adapt3R: ✅ Maintains IL performance on LIBERO & MimicGen benchmarks ✅ Outperforms DP3 and 3D Diffuser Actor in most settings ✅ Holds >80% success on LIBERO even with large camera rotations Thanks for sharing this, Animesh Garg & Albert Wilcox! 📍Paper: Website: Code:
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Most imitation learning policies break when the camera moves or the robot changes. NOT THIS ONE 👇 [📍 Bookmark for later ] A new 3D scene representation encoder, tackles this by enabling zero-shot generalization to unseen embodiments and viewpoints… And it works with any IL algorithm. The trick? •Use a 2D foundation model to extract semantic features •Lift them into 3D space for localization (not semantics) •Condition the IL policy on this spatially grounded vector Across 93 simulated and 6 real tasks, Adapt3R: ✅ Maintains IL performance on LIBERO & MimicGen benchmarks ✅ Outperforms DP3 and 3D Diffuser Actor in most settings ✅ Holds >80% success on LIBERO even with large camera rotations Thanks for sharing this, Animesh Garg & Albert Wilcox! 📍Paper: Website: Code:

Ilir Aliu

12,178 görüntüleme • 10 ay önce

Made $100K+ by building a simulation that predicts SPX price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.
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Made $100K+ by building a simulation that predicts SPX price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.

Himanshu Kumar

75,907 görüntüleme • 3 ay önce

"मोदी है तो मुमकिन है" Travelled in Vande Bharat, today from Surat to Gandhinagar. With a speed of 130Km/hr this train is a blessing for the passengers. Comfort✅ Speed✅ Cleanliness ✅
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"मोदी है तो मुमकिन है" Travelled in Vande Bharat, today from Surat to Gandhinagar. With a speed of 130Km/hr this train is a blessing for the passengers. Comfort✅ Speed✅ Cleanliness ✅

Pradipsinh Vaghela

11,090 görüntüleme • 3 yıl önce

🔎 Where I Find New OSINT Tools Most people spend hours searching for a specific OSINT tool. This is why I want to share with you websites that have almost all OSINT tools. All in one place. Here are 5 worth bookmarking right now. 👇 🗂️ TOOLBOXES ✅ OSINTRack - A growing OSINT tools directory with a clean interface. Good for discovering tools by category quickly. Website link: ✅ OSINT Tools Library - A maintained directory from the OSINT Newsletter. Every tool is tagged by access level (free, limited, or paid). The most actively updated resource of its kind. Website link: ✅ Bellingcat Toolkit - Bellingcat's curated collection of open source investigation tools, organized by category. A trusted resource from one of the most respected names in OSINT. Website link: ✅ OSINT Library - A community-built collection of OSINT tools and resources, organized by investigation type. Great for finding tools you have never heard of before. Website link: ✅ Open Source Intelligence Hub - A clean, no-frills index of OSINT tools and techniques. Useful for quickly scanning what is available across different investigation categories. Website link: __________ Do you know other toolboxes? Let me know in the comments. 👇 P.S. ♻️ Repost if you found this helpful.
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🔎 Where I Find New OSINT Tools Most people spend hours searching for a specific OSINT tool. This is why I want to share with you websites that have almost all OSINT tools. All in one place. Here are 5 worth bookmarking right now. 👇 🗂️ TOOLBOXES ✅ OSINTRack - A growing OSINT tools directory with a clean interface. Good for discovering tools by category quickly. Website link: ✅ OSINT Tools Library - A maintained directory from the OSINT Newsletter. Every tool is tagged by access level (free, limited, or paid). The most actively updated resource of its kind. Website link: ✅ Bellingcat Toolkit - Bellingcat's curated collection of open source investigation tools, organized by category. A trusted resource from one of the most respected names in OSINT. Website link: ✅ OSINT Library - A community-built collection of OSINT tools and resources, organized by investigation type. Great for finding tools you have never heard of before. Website link: ✅ Open Source Intelligence Hub - A clean, no-frills index of OSINT tools and techniques. Useful for quickly scanning what is available across different investigation categories. Website link: __________ Do you know other toolboxes? Let me know in the comments. 👇 P.S. ♻️ Repost if you found this helpful.

CyberSudo

11,205 görüntüleme • 1 ay önce