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Picking up laundry with a 3D printed robot arm and LeRobot. It operates using SO-ARM101 arm mounted on a mobile base, relying on just a wrist and base camera. The base is controlled with an Xbox controller by a human operator. The arm is fully autonomous thanks to the...

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LeRobot

20,155 subscribers

69,148 просмотров • 1 месяц назад •via X (Twitter)

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Фото профиля LeRobot
LeRobot1 месяц назад

Full video:

Фото профиля Shinka - AI
Shinka - AI1 месяц назад

A 3D printed arm trained on a single GPU doing laundry in a messy living room tells you more about real robotics progress than any million-dollar lab demo.

Фото профиля Steven Cheng
Steven Cheng1 месяц назад

Negative examples are key. They teach the model how to recover, which is crucial for messy real-world environments.

Фото профиля SEAR
SEAR1 месяц назад

Messy living room data is gold

Фото профиля tongtongtongtongtong
tongtongtongtongtong1 месяц назад

Neat!

Фото профиля primesync
primesync1 месяц назад

laundry is a brutal benchmark because cloth has no fixed shape, so nothing generalizes from rigid-object demos. doing it with just wrist and base cams on a 3D printed arm is the part that should get more attention.

Фото профиля Saquib Mehmood
Saquib Mehmood1 месяц назад

Cool.

Фото профиля Mario Bollini
Mario Bollini1 месяц назад

Do toddler toys next!

Фото профиля Ajit Sadalagi
Ajit Sadalagi1 месяц назад

Can this run end to end neural network locally ?

Фото профиля Raul Verdusco
Raul Verdusco1 месяц назад

Open tools are making real-world robotics more accessible.Generalizing to messy, unseen environments with consumer hardware is exactly the kind of progress that matters for practical home robotics. 🤖

Фото профиля Krafters Robotics
Krafters Robotics1 месяц назад

The human-driven base + autonomous arm split is a sensible way to isolate navigation from manipulation. The number I'd love to see next is interventions per garment, especially after a missed grasp or occlusion.

Фото профиля Daniel Lambert
Daniel Lambert1 месяц назад

The impressive part isn’t picking up laundry. It’s generalizing to new environments and recovering from failures. That’s a huge step toward practical home robots. 🧺🤖

Фото профиля Robo Reliance
Robo Reliance1 месяц назад

Messy living rooms and confusing shadows are the honest benchmark. We work the field side of robotics and the failure modes are never the ones the lab predicted. Recovery behavior is the real milestone here.

Фото профиля Lee Penkman
Lee Penkman1 месяц назад

Epic

Фото профиля Aeris Vahn Ephelia - 𝑹𝑬𝑽
Aeris Vahn Ephelia - 𝑹𝑬𝑽1 месяц назад

Congratulation this is nice work

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23,293 просмотров • 2 месяцев назад

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A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

The Humanoid Hub

20,968 просмотров • 1 год назад

Today we're announcing microSLAM, a monocular SLAM system built by our computer vision team in Zürich with ETH Zurich's Computer Vision and Geometry Lab. It ranks first on the LaMaria benchmark for monocular SLAM, and holds up against systems that carry many more cameras and IMUs. Ours runs on a single RGB stream. Robot learning is bottlenecked on data, and the largest untapped source is people going about their work. A camera on someone's head for an afternoon in a plant is a record of how that plant actually operates, including all the parts nobody writes down. That data is only useful if you can recover the geometry, and geometry from one moving camera in a world that will not sit still is the hard version of the problem. Solving it monocular is what makes the data cheap. Rigs do not scale to every worker. Glasses do. microSLAM turns that footage into interactive 3D environments where behavioral foundation models can be trained. One person walks a factory floor. A robot learns to navigate it.
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Today we're announcing microSLAM, a monocular SLAM system built by our computer vision team in Zürich with ETH Zurich's Computer Vision and Geometry Lab. It ranks first on the LaMaria benchmark for monocular SLAM, and holds up against systems that carry many more cameras and IMUs. Ours runs on a single RGB stream. Robot learning is bottlenecked on data, and the largest untapped source is people going about their work. A camera on someone's head for an afternoon in a plant is a record of how that plant actually operates, including all the parts nobody writes down. That data is only useful if you can recover the geometry, and geometry from one moving camera in a world that will not sit still is the hard version of the problem. Solving it monocular is what makes the data cheap. Rigs do not scale to every worker. Glasses do. microSLAM turns that footage into interactive 3D environments where behavioral foundation models can be trained. One person walks a factory floor. A robot learns to navigate it.

Gianni Hodel

68,719 просмотров • 22 дней назад

Every mobile robot has the same problem. The arms are too heavy. Too weak. Too dumb… You bolt on a robot designed for a factory floor and wonder why your AMR’s battery dies in 2 hours. The payload-to-weight ratio kills you. The arm weighs 20kg and lifts 5. Mobile manipulation? A forklift with a wrist! Eris and Dimitris Sako, two brothers in Zurich (Duatic), spent years inside ETH’s Robotic Systems Lab staring at this exact problem. Their answer: control the muscle. They built their own Quasi-Direct Drive actuators from scratch. And an arm around them. And a robot around the arm. The DynaArm weighs 9 kg. Lifts 12. IP66. Backdrivable!!! …meaning it feels contact like a human arm does. Payload-to-weight ratio is inverted….the arm lifts more than itself! Their Alpha robot puts two of these on a wheeled base that carries 200 kg. Vision-guided. Hot-swappable batteries. Built for floor-to-shelf, not for selfies at trade shows (even though those are pretty good too). A machine that actually works in a warehouse. This is the European robotics playbook done right: deep hardware R&D → vertical integration → real product. Watch these two, and their team. I’m a huge fan of their work! 👋 —— Weekly robotics and AI insights. Subscribe free:
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Every mobile robot has the same problem. The arms are too heavy. Too weak. Too dumb… You bolt on a robot designed for a factory floor and wonder why your AMR’s battery dies in 2 hours. The payload-to-weight ratio kills you. The arm weighs 20kg and lifts 5. Mobile manipulation? A forklift with a wrist! Eris and Dimitris Sako, two brothers in Zurich (Duatic), spent years inside ETH’s Robotic Systems Lab staring at this exact problem. Their answer: control the muscle. They built their own Quasi-Direct Drive actuators from scratch. And an arm around them. And a robot around the arm. The DynaArm weighs 9 kg. Lifts 12. IP66. Backdrivable!!! …meaning it feels contact like a human arm does. Payload-to-weight ratio is inverted….the arm lifts more than itself! Their Alpha robot puts two of these on a wheeled base that carries 200 kg. Vision-guided. Hot-swappable batteries. Built for floor-to-shelf, not for selfies at trade shows (even though those are pretty good too). A machine that actually works in a warehouse. This is the European robotics playbook done right: deep hardware R&D → vertical integration → real product. Watch these two, and their team. I’m a huge fan of their work! 👋 —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

25,966 просмотров • 6 месяцев назад

OpenAI's thijs predicts robotics will have its own version of the 3D printing craze, with people buying cheap, open-source robot arms and connecting them to agents. "I can see a world quite soon where, similar to when everyone went and bought 3D printers and ran software, everyone's going to buy plastic, cheaper robot arms that you can clip onto a table, put stuff in front of, and plug them into agents." "The price of classic robotics equipment is thousands of dollars at the moment. This arm I'm using here is a Hugging Face SO-100 robot, which is open source, fully 3D printable, and around $200." "People can just pull up Codex and start controlling robots with it right now."
1:37

OpenAI's thijs predicts robotics will have its own version of the 3D printing craze, with people buying cheap, open-source robot arms and connecting them to agents. "I can see a world quite soon where, similar to when everyone went and bought 3D printers and ran software, everyone's going to buy plastic, cheaper robot arms that you can clip onto a table, put stuff in front of, and plug them into agents." "The price of classic robotics equipment is thousands of dollars at the moment. This arm I'm using here is a Hugging Face SO-100 robot, which is open source, fully 3D printable, and around $200." "People can just pull up Codex and start controlling robots with it right now."

TBPN

149,443 просмотров • 21 дней назад

you don’t need a robot to see physical ai in action, you just need a browser. this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser. the result is the first ever demo of ACT that runs locally on the web. try it yourself now at
0:35

you don’t need a robot to see physical ai in action, you just need a browser. this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser. the result is the first ever demo of ACT that runs locally on the web. try it yourself now at

Binh

122,572 просмотров • 3 месяцев назад

1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group Zhuoyuan Wu! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: 🌐 Project: 💻 Code: 🤗 Robot data: 🤗 Human data: 🤗 Weights: #Robotics #WorldModels #AI #OpenSource
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1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group Zhuoyuan Wu! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: 🌐 Project: 💻 Code: 🤗 Robot data: 🤗 Human data: 🤗 Weights: #Robotics #WorldModels #AI #OpenSource

Jun Gao

105,082 просмотров • 3 месяцев назад

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16:27

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

17,001 просмотров • 5 месяцев назад

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵
2:21

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Jim Fan

467,416 просмотров • 1 год назад

Train robots. Earn rewards. Base is pioneering the Train-to-Earn model for robotics. With Axis Robotics, anyone can help train robot simulations from a laptop and be rewarded for contributing data. The future of robotics won’t be trained by a handful of companies. It’ll be trained by everyone. PL 漂亮 (✱,✱) showcased this vision at SuperAI, the largest AI event in Asia 👇
1:00

Train robots. Earn rewards. Base is pioneering the Train-to-Earn model for robotics. With Axis Robotics, anyone can help train robot simulations from a laptop and be rewarded for contributing data. The future of robotics won’t be trained by a handful of companies. It’ll be trained by everyone. PL 漂亮 (✱,✱) showcased this vision at SuperAI, the largest AI event in Asia 👇

Base APAC

18,980 просмотров • 3 месяцев назад