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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

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67,753 views • 10 days ago •via X (Twitter)

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The $600 Open-Source Home Robot🤖 Meet AlohaMini, the new dual-arm mobile robot designed to make real-world manipulation and embodied AI research accessible. This bot is fully 3D-printable and aimed at home builders and research labs. Key Features: 💰 Cost: The Bill of Materials (BOM) totals around $600 (USD) for self-printed parts, making it highly accessible. 🦾 Hardware: Dual-arm mobile base with a motorized vertical lift (0–60 cm travel). 💻 Software: Completely open-source hardware and software, compatible out-of-the-box with the LeRobot framework. The platform supports a fully 3D-printed Mini and a stiffer Pro model (hybrid materials). It assembles in approximately 60 minutes. 🔗 GitHub Open-Source Code & Files:
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The $600 Open-Source Home Robot🤖 Meet AlohaMini, the new dual-arm mobile robot designed to make real-world manipulation and embodied AI research accessible. This bot is fully 3D-printable and aimed at home builders and research labs. Key Features: 💰 Cost: The Bill of Materials (BOM) totals around $600 (USD) for self-printed parts, making it highly accessible. 🦾 Hardware: Dual-arm mobile base with a motorized vertical lift (0–60 cm travel). 💻 Software: Completely open-source hardware and software, compatible out-of-the-box with the LeRobot framework. The platform supports a fully 3D-printed Mini and a stiffer Pro model (hybrid materials). It assembles in approximately 60 minutes. 🔗 GitHub Open-Source Code & Files:

RoboHub🤖

41,670 views • 9 months ago

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:
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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

32,025 views • 6 months ago

introducing lattice a low-cost robot arm with a modular, detachable gripper so the robot can swap its own tools the adapter is $14 if you already have an so101 arm, and about $140 to build the full system from scratch it’s now fully open source
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introducing lattice a low-cost robot arm with a modular, detachable gripper so the robot can swap its own tools the adapter is $14 if you already have an so101 arm, and about $140 to build the full system from scratch it’s now fully open source

ahad

40,184 views • 2 months ago

🔥 Community spotlight! Haixuan Tao built XoQ — a robot virtualization library that lets you connect to OpenArm from any OS, including macOS and even the web browser! Works with LeRobot for teleoperation. This is what open source robotics is all about 🤖
0:20

🔥 Community spotlight! Haixuan Tao built XoQ — a robot virtualization library that lets you connect to OpenArm from any OS, including macOS and even the web browser! Works with LeRobot for teleoperation. This is what open source robotics is all about 🤖

Enactic

21,271 views • 6 months ago

💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.
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💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.

Ilir Aliu - eu/acc

17,353 views • 11 months ago

Below is our open-source arm assembled by one of the discord members! Also, today Physical Intelligence shared their incredible top-notch robotics model Pi0. And I just want to remind you that you can use our arm together with this model and with LeRobot The arm has the same DOF as the arm used by Pi0, but has smaller size and cost starts from 200$ per arm. You can use better motors like we did in our demos, then it will be more precise and stable but the price will be about $600 per arm. Printable files, purchase list, and help with the assembly - in our discord channel.
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Below is our open-source arm assembled by one of the discord members! Also, today Physical Intelligence shared their incredible top-notch robotics model Pi0. And I just want to remind you that you can use our arm together with this model and with LeRobot The arm has the same DOF as the arm used by Pi0, but has smaller size and cost starts from 200$ per arm. You can use better motors like we did in our demos, then it will be more precise and stable but the price will be about $600 per arm. Printable files, purchase list, and help with the assembly - in our discord channel.

Igor Kulakov

53,711 views • 1 year ago

VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.
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VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

RoboHub🤖

12,961 views • 9 months ago

MolmoAct2 runs zero-shot on an SO-ARM101, no training required 🦾 Ai2 open Action Reasoning Model is now in LeRobot with the full lifecycle covered: fine-tuning (full or LoRA), evaluation, and real-robot deployment. It pairs a Molmo2-ER vision-language backbone with a flow-matching action expert to turn images, language, and proprioceptive state into action chunks. The best part: ready-made checkpoints ship with calibration correction baked in, so you can point it at your SO-100/101 and just watch it work. Inference fits in ~12GB at bf16, and LoRA fine-tuning runs on a single 24GB GPU. Big thanks to the Ai2 team for building this in the open.
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MolmoAct2 runs zero-shot on an SO-ARM101, no training required 🦾 Ai2 open Action Reasoning Model is now in LeRobot with the full lifecycle covered: fine-tuning (full or LoRA), evaluation, and real-robot deployment. It pairs a Molmo2-ER vision-language backbone with a flow-matching action expert to turn images, language, and proprioceptive state into action chunks. The best part: ready-made checkpoints ship with calibration correction baked in, so you can point it at your SO-100/101 and just watch it work. Inference fits in ~12GB at bf16, and LoRA fine-tuning runs on a single 24GB GPU. Big thanks to the Ai2 team for building this in the open.

LeRobot

22,200 views • 1 month ago

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄
0:32

What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄

NVIDIA Robotics

22,882 views • 1 month ago

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.
2:05

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 views • 1 year ago

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 views • 4 months ago

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 Pham

121,383 views • 1 month ago

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
0:31

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

104,664 views • 2 months ago

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 views • 1 month ago

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

466,442 views • 1 year ago

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.
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 views • 3 months ago

Quadrupeds are fast. Agile. Great at locomotion. But can they manipulate? A new approach from Carnegie Mellon University, Google DeepMind, and Bosch is teaching quadrupedal robots to do more than walk, they’re learning to interact. It’s called Human2LocoMan: a system that uses human data to pretrain robot policies before finetuning on real hardware. The result? A four-legged robot that can walk, carry, organize, scoop, and sort with both single and dual-arm control. By pretraining on human motion, they cut the amount of robot data in half—while improving success rates by over 80% in unfamiliar environments. Their Modularized Cross-Embodiment Transformer (MXT) learns from both human and robot demonstrations, then generalizes those skills to physical tasks—no hardcoded behaviors required. It’s locomotion and manipulation. A quadruped that can walk and clean up after itself?
1:21

Quadrupeds are fast. Agile. Great at locomotion. But can they manipulate? A new approach from Carnegie Mellon University, Google DeepMind, and Bosch is teaching quadrupedal robots to do more than walk, they’re learning to interact. It’s called Human2LocoMan: a system that uses human data to pretrain robot policies before finetuning on real hardware. The result? A four-legged robot that can walk, carry, organize, scoop, and sort with both single and dual-arm control. By pretraining on human motion, they cut the amount of robot data in half—while improving success rates by over 80% in unfamiliar environments. Their Modularized Cross-Embodiment Transformer (MXT) learns from both human and robot demonstrations, then generalizes those skills to physical tasks—no hardcoded behaviors required. It’s locomotion and manipulation. A quadruped that can walk and clean up after itself?

Lukas Ziegler

61,610 views • 1 year ago

The quadruped you can assemble yourself. Stella is an educational quadruped robot developed by Ahead. Designed for hands-on learning and self-assembly, the robot uses durable 3D-printed components. Ideal for all education levels, it is easy to repair, infinitely extendable, and equipped with advanced hardware and software. Despite its compact design, Stella is versatile, capable of operating in various environments and even carrying payloads like an optional robotic arm. A powerful tool for exploring robotics.
0:32

The quadruped you can assemble yourself. Stella is an educational quadruped robot developed by Ahead. Designed for hands-on learning and self-assembly, the robot uses durable 3D-printed components. Ideal for all education levels, it is easy to repair, infinitely extendable, and equipped with advanced hardware and software. Despite its compact design, Stella is versatile, capable of operating in various environments and even carrying payloads like an optional robotic arm. A powerful tool for exploring robotics.

Circuit

23,215 views • 1 year ago