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Train in sim. Run it for real. Teach it new tricks. 🦆 This is the sim2real loop that powers Microduck. RL stack open source here : Buy it here : Git : Join our community :

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Pollen Robotics

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247,984 görüntüleme • 1 gün önce •via X (Twitter)

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We built a small biped robot you can teach new tricks to. Train it in simulation, run it on the real thing. Meet Microduck 🦆 $399, shipping before Christmas.
0:51

We built a small biped robot you can teach new tricks to. Train it in simulation, run it on the real thing. Meet Microduck 🦆 $399, shipping before Christmas.

Pollen Robotics

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🤖WILD: This $399 robot duck is selling one unit every four seconds. Hugging Face just launched the "Microduck," an open-source robot anyone can teach new tricks. It can waddle, roller skate, pick things up with its beak, and get back up when it falls.
0:51

🤖WILD: This $399 robot duck is selling one unit every four seconds. Hugging Face just launched the "Microduck," an open-source robot anyone can teach new tricks. It can waddle, roller skate, pick things up with its beak, and get back up when it falls.

Coin Bureau

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Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen, Gemma at cluster scale on rented GPUs. That loop is why open reasoning models are catching closed labs without owning a data center. Prime-RL + verifiers + distributed rollouts + LLM judges + multi-cloud GPUs - that's the stack. Watch and save it, then launch your first distributed RL run this week.
30:09

Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen, Gemma at cluster scale on rented GPUs. That loop is why open reasoning models are catching closed labs without owning a data center. Prime-RL + verifiers + distributed rollouts + LLM judges + multi-cloud GPUs - that's the stack. Watch and save it, then launch your first distributed RL run this week.

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Here is the video for our new single please presave it here
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Here is the video for our new single please presave it here

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Our new sponsor SoundMoney is bringing gold to the people. Buy real gold and silver from $1. Store it in America. Sell it with one click. Support our work and get your FREE account here:
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Our new sponsor SoundMoney is bringing gold to the people. Buy real gold and silver from $1. Store it in America. Sell it with one click. Support our work and get your FREE account here:

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Launching SYNTHETIC-2: our next-gen open reasoning dataset and planetary-scale synthetic data generation run. Powered by our P2P inference stack and DeepSeek-R1-0528, it verifies traces for the hardest RL tasks. Contribute towards AGI via open, permissionless compute.
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Launching SYNTHETIC-2: our next-gen open reasoning dataset and planetary-scale synthetic data generation run. Powered by our P2P inference stack and DeepSeek-R1-0528, it verifies traces for the hardest RL tasks. Contribute towards AGI via open, permissionless compute.

Prime Intellect

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This guy explained agent harness, memory, and loop engineering in one video - clearer than most paid courses a raw LLM with no harness is a wild horse. all power, no direction > trace the run -> score it with an LLM -> diagnose the bottleneck -> patch it -> ship it that loop is what lets an agent get sharper on its own, run after run memory + harness + loop engineering + evals - that's the whole stack in one picture watch it, then grab the diagram below
17:43

This guy explained agent harness, memory, and loop engineering in one video - clearer than most paid courses a raw LLM with no harness is a wild horse. all power, no direction > trace the run -> score it with an LLM -> diagnose the bottleneck -> patch it -> ship it that loop is what lets an agent get sharper on its own, run after run memory + harness + loop engineering + evals - that's the whole stack in one picture watch it, then grab the diagram below

Mr. Buzzoni

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AI is concentrating, in a few countries, a few companies, a few chips. Open-source AI is the only real check on that. And it only works if the infrastructure to run it actually exists. Today, we're announcing our Series A, $20 M, to make this a reality.
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AI is concentrating, in a few countries, a few companies, a few chips. Open-source AI is the only real check on that. And it only works if the infrastructure to run it actually exists. Today, we're announcing our Series A, $20 M, to make this a reality.

PicoCreator - AI builder @ 🌉

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The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.
1:01

The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.

Praveen Kumar Verma

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The Pact is here, and with it comes our new open-world activity Retaliation. Here's a peek at how it works!
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The Pact is here, and with it comes our new open-world activity Retaliation. Here's a peek at how it works!

Tom Clancy's The Division

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Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA
1:21

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

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I rebuilt most of OpenClaw's core in a single workflow: - 25 blocks - 29 connections - Short + long-term memory - Multi-channel (Telegram + Slack) Didn't build it manually. Stack is fully open-source. Self-host, run local models, own it end-to-end. Full walkthrough: Chapters: 00:00 - Intro 01:00 - SimClaw in action: planning my day, finding meetings, sending email 04:05 - Long-term memory capability 05:52 - Inside the workflow: how it's wired 12:09 - The plot twist 12:50 - Building an entire workflow using a single prompt 15:42 - Why this is an OS for your AI workforce 17:00 - Try it yourself If you want to see the open-source stack that powers all of this, check out Sim on GitHub and drop a star if you find it useful:
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I rebuilt most of OpenClaw's core in a single workflow: - 25 blocks - 29 connections - Short + long-term memory - Multi-channel (Telegram + Slack) Didn't build it manually. Stack is fully open-source. Self-host, run local models, own it end-to-end. Full walkthrough: Chapters: 00:00 - Intro 01:00 - SimClaw in action: planning my day, finding meetings, sending email 04:05 - Long-term memory capability 05:52 - Inside the workflow: how it's wired 12:09 - The plot twist 12:50 - Building an entire workflow using a single prompt 15:42 - Why this is an OS for your AI workforce 17:00 - Try it yourself If you want to see the open-source stack that powers all of this, check out Sim on GitHub and drop a star if you find it useful:

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also check out our code here we haven't released everything yet but there's enough there that you can train a policy, eval it in sim, and view the rollouts live in viser!
0:50

also check out our code here we haven't released everything yet but there's enough there that you can train a policy, eval it in sim, and view the rollouts live in viser!

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The discussion of the rapture of the church is a source of joy for us, as it reaffirms that our place isn't here on Earth. This brings comfort to our hearts.
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The discussion of the rapture of the church is a source of joy for us, as it reaffirms that our place isn't here on Earth. This brings comfort to our hearts.

Huiothesia

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"It is Jews! right here in Harlem who run these whisky-stores that get you drunk, It is Jews that run these old run-down stores that sell you bad food. It is Jews who control the economy of Harlem and use it for themselves and for the benefit of Israel." - Malcolm X
1:39

"It is Jews! right here in Harlem who run these whisky-stores that get you drunk, It is Jews that run these old run-down stores that sell you bad food. It is Jews who control the economy of Harlem and use it for themselves and for the benefit of Israel." - Malcolm X

Taro Wheel

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Tired of waiting hours for humanoids to learn to walk? Our new technical report shows how to train sim-to-real humanoid locomotion in 15 minutes with FastSAC and FastTD3! The full pipeline is open-source in the newly released Holosoma codebase. Thread 🧵
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Tired of waiting hours for humanoids to learn to walk? Our new technical report shows how to train sim-to-real humanoid locomotion in 15 minutes with FastSAC and FastTD3! The full pipeline is open-source in the newly released Holosoma codebase. Thread 🧵

Younggyo Seo

36,937 görüntüleme • 8 ay önce

Asimov is now zero-shot sim2real for locomotion. You train the policy in simulation. It runs on the robot without additional tuning. Blog: Order yours:
0:32

Asimov is now zero-shot sim2real for locomotion. You train the policy in simulation. It runs on the robot without additional tuning. Blog: Order yours:

Asimov

16,043 görüntüleme • 2 gün önce

Our new trailer has been revealed! In case you missed it live – here it is for your viewing pleasure! 📽️
1:40

Our new trailer has been revealed! In case you missed it live – here it is for your viewing pleasure! 📽️

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