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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...

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Ilir Aliu

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32,025 次观看 • 4 个月前 •via X (Twitter)

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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:
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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,391 次观看 • 4 个月前

TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.
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TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

The Humanoid Hub

46,715 次观看 • 1 年前

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 次观看 • 6 个月前

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork
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We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

185,727 次观看 • 2 个月前

Open-source dexterous hands with fingertip sensors! 🪬 ORCA Dexterity just released three dexterous hand models. Their mission: is to democratize robotic hand dexterity. They're sharing progress on exceptional, low-cost hardware and a software layer from low-level control to robotic hand learning. This is the open-source approach to dexterous manipulation. It has 83 taxels per finger with 0.1 N force detection which pretty is impressive for an open-source design. Tactile sensing is critical for dexterous manipulation, knowing contact forces enables gentle grasping, slip detection, and force-controlled assembly. Also 700g weight for the lite version makes it practical for mounting on robot arms without exceeding payload limits. Lower weight means faster movements and lower torque requirements. Open hardware accelerates robotics by letting researchers and builders modify designs for their specific needs without starting from scratch! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Open-source dexterous hands with fingertip sensors! 🪬 ORCA Dexterity just released three dexterous hand models. Their mission: is to democratize robotic hand dexterity. They're sharing progress on exceptional, low-cost hardware and a software layer from low-level control to robotic hand learning. This is the open-source approach to dexterous manipulation. It has 83 taxels per finger with 0.1 N force detection which pretty is impressive for an open-source design. Tactile sensing is critical for dexterous manipulation, knowing contact forces enables gentle grasping, slip detection, and force-controlled assembly. Also 700g weight for the lite version makes it practical for mounting on robot arms without exceeding payload limits. Lower weight means faster movements and lower torque requirements. Open hardware accelerates robotics by letting researchers and builders modify designs for their specific needs without starting from scratch! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

37,985 次观看 • 2 个月前

Open Source Robot Company K-Scale Labs has announced a ~$9,000 personal robot. The entire robotics open source system: hardware, software, and machine learning infrastructure, all tightly integrated and ready for your use case. I am placing an order. I will also order Optimus.
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Open Source Robot Company K-Scale Labs has announced a ~$9,000 personal robot. The entire robotics open source system: hardware, software, and machine learning infrastructure, all tightly integrated and ready for your use case. I am placing an order. I will also order Optimus.

Brian Roemmele

173,836 次观看 • 1 年前

𝗢𝗽𝗲𝗻𝗔𝗿𝗺 is an open-source robot arm designed for human manipulation data collection. What's new in 𝘃𝟬.𝟮 (beta): ・Teleop with gravity compensation ・Bilateral force feedback support #OpenSource #Robotics #Humanoids #ArtificialIntelligence
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𝗢𝗽𝗲𝗻𝗔𝗿𝗺 is an open-source robot arm designed for human manipulation data collection. What's new in 𝘃𝟬.𝟮 (beta): ・Teleop with gravity compensation ・Bilateral force feedback support #OpenSource #Robotics #Humanoids #ArtificialIntelligence

Reazon Human Interaction Lab

43,128 次观看 • 1 年前

A high-quality haptic control for robots as a weekend project? All for under $600. [📍 Save Github & arXiv for later] A team built DOGlove, an open-source haptic glove for robot teleoperation that you can assemble yourself for under $600. It is built for real control, not demos. • Tracks 21 degrees of freedom with a custom joint design • Provides cable-driven force feedback plus fingertip haptics • Can be used to collect data for imitation learning Affordable haptics could make teleoperation more reliable and improve the quality of data we use to train physical AI. Project link: Credit: DOGlove team, shared by Han Zhang —- Weekly robotics and AI insights. Subscribe free:
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A high-quality haptic control for robots as a weekend project? All for under $600. [📍 Save Github & arXiv for later] A team built DOGlove, an open-source haptic glove for robot teleoperation that you can assemble yourself for under $600. It is built for real control, not demos. • Tracks 21 degrees of freedom with a custom joint design • Provides cable-driven force feedback plus fingertip haptics • Can be used to collect data for imitation learning Affordable haptics could make teleoperation more reliable and improve the quality of data we use to train physical AI. Project link: Credit: DOGlove team, shared by Han Zhang —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

18,290 次观看 • 4 个月前

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 次观看 • 7 个月前

Robotics is still data starved. Collecting high-quality robot demonstrations remains brutally slow and expensive. Introducing COBALT: A cloud-native teleoperation platform designed for large-scale robot learning. We are democratizing data collection by leveraging the hardware everyone already owns: the smartphone All you need is to download an app (today)! Read on for more!
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Robotics is still data starved. Collecting high-quality robot demonstrations remains brutally slow and expensive. Introducing COBALT: A cloud-native teleoperation platform designed for large-scale robot learning. We are democratizing data collection by leveraging the hardware everyone already owns: the smartphone All you need is to download an app (today)! Read on for more!

Animesh Garg

85,683 次观看 • 7 天前

We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”
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We asked Angus about the future of robotics and AI training. “I built an industrial-grade kinematic solver for 6 degrees of freedom motion, with full path planning and joint control." "You can stream AI model outputs into it, or run it like a traditional industrial robot." "The benefit of that is all of that information you can stream and record from those joints… you can use that as the training data." "Frankly, moving a robot with a lever is really crap data for training robots. But this gives you smooth, high-quality motion, without needing two robots.”

TBPN

17,077 次观看 • 11 个月前

Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉
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Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉

Max Fu

69,224 次观看 • 1 年前

The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.
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The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.

The Humanoid Hub

44,441 次观看 • 3 个月前

SOMEONE JUST BUILT AN OPERATING SYSTEM FOR ROBOTS not a framework... not a library an actual OS dimos is an open-source platform that lets you build, run, and deploy software on any robot humanoids, quadrupeds, drones, wheeled bots you write python modules .... they talk to each other via typed streams...just like microservices, but for physical hardware the wild part..? you can give your AI editor (cursor, claude code) MCP access to send real physical commands "move forward 1 meter" → robot moves. it ships with a full navigation stack SLAM, collision avoidance, route planning. plus a dashboard to debug, visualize, and control your robot in real-time via waypoints, VR, or keyboard. supported hardware already includes unitree G1 humanoids, dji drones, and multiple robot arms. programming physical robots is finally as simple as programming software
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SOMEONE JUST BUILT AN OPERATING SYSTEM FOR ROBOTS not a framework... not a library an actual OS dimos is an open-source platform that lets you build, run, and deploy software on any robot humanoids, quadrupeds, drones, wheeled bots you write python modules .... they talk to each other via typed streams...just like microservices, but for physical hardware the wild part..? you can give your AI editor (cursor, claude code) MCP access to send real physical commands "move forward 1 meter" → robot moves. it ships with a full navigation stack SLAM, collision avoidance, route planning. plus a dashboard to debug, visualize, and control your robot in real-time via waypoints, VR, or keyboard. supported hardware already includes unitree G1 humanoids, dji drones, and multiple robot arms. programming physical robots is finally as simple as programming software

Vaishnavi

19,642 次观看 • 1 个月前

the conference is getting crazy over it today we're unveiling our 1st robot Hugging Face 🤝 Pollen Robotics a low-cost $250 open-source robot designed as an open-source platform for fun human computer interactions powered by HF Spaces-models-community >
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the conference is getting crazy over it today we're unveiling our 1st robot Hugging Face 🤝 Pollen Robotics a low-cost $250 open-source robot designed as an open-source platform for fun human computer interactions powered by HF Spaces-models-community >

Thomas Wolf

104,820 次观看 • 1 年前

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
1:23

Feeling what a robot feels is becoming real! 🫰 Fluid Reality just ran its first full end-to-end touch teleoperation system, and the demo shows why high-resolution haptics might be the missing piece in robot control. A 22 mm fingertip display with 32 independently actuated “bubbles” was mapped directly to tactile sensors on a robot hand. 🫧 Three fingers streamed real-time touch data robot → human, giving the operator actual contact feedback instead of waving their hands blind, like most teleop systems today. It’s still early, but this is a real step toward dexterous ops and high-quality data for training Physical AI systems. Companies involved: Samsung Electronics, Alt-Bionics, Inc. (the robot hand), MANUS™ (hand tracking), and Sensible Robotics (fingertip sensors) Touch isn’t a nice-to-have in robotics. It’s the difference between manipulation that works in the real world P.S. Guess how many touch receptors we humans, have in our hand! :) ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

46,829 次观看 • 6 个月前

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

23,213 次观看 • 1 年前

Can we collect robot data without any robots? Introducing Universal Manipulation Interface (UMI) An open-source $400 system from Stanford University designed to democratize robot data collection 0 teleop -> autonomously wash dishes (precise), toss (dynamic), and fold clothes (bimanual)
1:16

Can we collect robot data without any robots? Introducing Universal Manipulation Interface (UMI) An open-source $400 system from Stanford University designed to democratize robot data collection 0 teleop -> autonomously wash dishes (precise), toss (dynamic), and fold clothes (bimanual)

Cheng Chi

438,672 次观看 • 2 年前

Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand. In VideoManip, Hongyi Chen and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies. Watch episode #73 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, now to learn more!
44:20

Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand. In VideoManip, Hongyi Chen and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies. Watch episode #73 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, now to learn more!

RoboPapers

27,249 次观看 • 1 个月前

Can we use wearable devices to collect robot data without actual robots? Yes! With a pair of gloves🧤! Introducing DexCap, a portable hand motion capture system that collects 3D data (point cloud + finger motion) for training robots with dexterous hands Everything open-sourced
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Can we use wearable devices to collect robot data without actual robots? Yes! With a pair of gloves🧤! Introducing DexCap, a portable hand motion capture system that collects 3D data (point cloud + finger motion) for training robots with dexterous hands Everything open-sourced

Chen Wang

234,658 次观看 • 2 年前