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DEXOP is a passive hand exoskeleton for collecting rich sensory data from vision, tactile, and proprioception. A kinematically equivalent robotic hand, EyeSight, was installed on a Unitree H1 to evaluate policies trained on DEXOP data.

The Humanoid Hub

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80,206 次观看 • 1 年前 •via X (Twitter)

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There are many ways to collect high-quality data. GenRobot seems to be closer to human-like methods. It uses a wearable exoskeleton robotic hand: a person simply wears the Gen DAS Dex and can flexibly complete various tasks, while simultaneously capturing the entire human motion trajectory and converting it into high-quality data for use by the humanoid robot. >23 degrees of freedom omnidirectional hand capture with millimeter-level precision >Tactile and visual fusion >Sub-millisecond head-hand synchronization

There are many ways to collect high-quality data. GenRobot seems to be closer to human-like methods. It uses a wearable exoskeleton robotic hand: a person simply wears the Gen DAS Dex and can flexibly complete various tasks, while simultaneously capturing the entire human motion trajectory and converting it into high-quality data for use by the humanoid robot. >23 degrees of freedom omnidirectional hand capture with millimeter-level precision >Tactile and visual fusion >Sub-millisecond head-hand synchronization

CyberRobo

18,098 次观看 • 5 个月前

This dexterous robotic hand can see, feel, and solve a Rubik’s cube BrainCo's Revo 3 dexterous robotic hand, a Chinese-developed system with integrated vision, full-palm tactile sensors, and high-precision actuators for complex manipulation tasks

This dexterous robotic hand can see, feel, and solve a Rubik’s cube BrainCo's Revo 3 dexterous robotic hand, a Chinese-developed system with integrated vision, full-palm tactile sensors, and high-precision actuators for complex manipulation tasks

Science girl

19,923 次观看 • 5 个月前

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means rethinking the whole stack from the ground up: - A robotics-native foundation model. - A 1:1 human-like robotic hand. - A noninvasive data collection glove for motion, force, and touch. - A simulator that turns weeks of experiments into minutes. GENE-26.5 is trained across language, vision, proprioception, tactile, and action. We designed a set of tasks to test how far we can go with this new paradigm. Fully autonomous, 1x speed, one model, same weights. (Enjoy with sound on) We are approaching the endgame for robotics. And this is just a beginning.

Genesis AI

2,741,542 次观看 • 4 个月前

Impressive on every dimension! Genesis AI has announced GENE-26.5, its first robotic foundation model, aimed at achieving human-level dexterous manipulation: - Single model, shared weights, handles egg cracking, lab pipetting, bimanual Rubik's Cube, smoothie making, and wire harnessing. Most tasks: under 1 hour of task-specific data, 1x real-world speed. - Genesis Hand 1.0: human-size, 20 active DoF, soft-contact skin. Paired glove gives a 1:1:1 mapping (glove, human hand, robot hand). 100x cheaper than teleop, 5x more data-efficient internally. - Data engine pulls from three sources: glove data, egocentric (head-cam) video, and large-scale internet video. The 1:1 hand-to-human match closes the embodiment gap, letting Genesis use video data more effectively than rivals. - Flow matching across vision, tactile, proprioception, and language inputs. Per Genesis, scaling data and compute directly improves zero-shot

Impressive on every dimension! Genesis AI has announced GENE-26.5, its first robotic foundation model, aimed at achieving human-level dexterous manipulation: - Single model, shared weights, handles egg cracking, lab pipetting, bimanual Rubik's Cube, smoothie making, and wire harnessing. Most tasks: under 1 hour of task-specific data, 1x real-world speed. - Genesis Hand 1.0: human-size, 20 active DoF, soft-contact skin. Paired glove gives a 1:1:1 mapping (glove, human hand, robot hand). 100x cheaper than teleop, 5x more data-efficient internally. - Data engine pulls from three sources: glove data, egocentric (head-cam) video, and large-scale internet video. The 1:1 hand-to-human match closes the embodiment gap, letting Genesis use video data more effectively than rivals. - Flow matching across vision, tactile, proprioception, and language inputs. Per Genesis, scaling data and compute directly improves zero-shot

The Humanoid Hub

95,894 次观看 • 4 个月前

A good hand can push intelligence development. Introducing Eyesight Hand, equipped with full-hand high-res tactile sensors and proprioceptive actuators. It is compliant, agile, and powerful. Good tactile sensing makes learning more efficient and robust. Shout out to Branden!

A good hand can push intelligence development. Introducing Eyesight Hand, equipped with full-hand high-res tactile sensors and proprioceptive actuators. It is compliant, agile, and powerful. Good tactile sensing makes learning more efficient and robust. Shout out to Branden!

Hao-Shu Fang

23,555 次观看 • 1 年前

Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.

Today at Meta FAIR we’re announcing three new cutting-edge developments in robotics and touch perception — and releasing a collection of artifacts to empower the community to build on this work. Details on all of this new work ➡️ 1️⃣ Meta Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks. Trained on 460K+ tactile images using self-supervised learning. 2️⃣ Meta Digit 360 is a breakthrough artificial fingertip-based tactile sensor, equipped with 18+ sensing features to deliver detailed touch data with human-level precision and touch-sensing capabilities. 3️⃣ Meta Digit Plexus is a standardized platform for robotic sensor connections and interactions. It provides a hardware-software solution to integrate tactile sensors on a single robot hand and enables seamless data collection, control and analysis over a single cable. The potential impact of expanding capabilities and components like these for the open source community ranges from medical research to supply chain, manufacturing and much more. We’re excited to continue this work with the broader community.

AI at Meta

453,518 次观看 • 1 年前

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

Lerrel Pinto

84,611 次观看 • 3 年前

RL-trained in-hand skills, such as grasp stability and object rotation are used to assist teleoperation. Teleoperation data is then used to train a VLA that fuses force and tactile feedback to generate actions. Humanoid hardware: Sharpa Paper:

RL-trained in-hand skills, such as grasp stability and object rotation are used to assist teleoperation. Teleoperation data is then used to train a VLA that fuses force and tactile feedback to generate actions. Humanoid hardware: Sharpa Paper:

The Humanoid Hub

11,614 次观看 • 6 个月前

🤖 INTERESTING: RLWRLD is collecting real-world dexterity data using MANUS gloves to train physical AI systems on human hand and finger movements.

🤖 INTERESTING: RLWRLD is collecting real-world dexterity data using MANUS gloves to train physical AI systems on human hand and finger movements.

Cointelegraph

26,524 次观看 • 4 个月前

Reward AI just came out of stealth and unveiled OM-1, a robot policy learned entirely from humans. No teleoperation, no on-robot data. One Model, Any Body. 1. Capture. A wearable 7-DoF hand (Omnibody Hand) lets people demonstrate manipulation naturally while working, cooking, or sorting. Sensors record tactile, proximity, force and hand pose at full human speed. 2. Learn. OM-1 trains on this human data alone. No teleop, no on-robot experience. Then it runs across bodies from industrial arms to humanoids, and scaling is just adding more human data. 3. Control. A high-frequency control layer, trained with RL in sim, executes those actions on any robot, holding steady through unknown loads like a stuck fridge door. The result: OM-1 picks up a brand-new task from under 30 minutes of data.

The Humanoid Hub

35,064 次观看 • 10 天前

Germany-based Agile Robots has introduced its humanoid robot, Agile One, designed for industrial environments. It features a dexterous robotic hand and runs on a physical AI system trained using real factory data, allowing it to work and interact autonomously. The company is now aiming to move Agile One into full-scale production.

Germany-based Agile Robots has introduced its humanoid robot, Agile One, designed for industrial environments. It features a dexterous robotic hand and runs on a physical AI system trained using real factory data, allowing it to work and interact autonomously. The company is now aiming to move Agile One into full-scale production.

Space and Technology

37,743 次观看 • 5 个月前

At Eastworlds, we validate our robot data before we sell it. Here, a Unitree G1 autonomously and reliably picks up a bottle using a model trained on Eastworlds' data with just $200 of compute.

At Eastworlds, we validate our robot data before we sell it. Here, a Unitree G1 autonomously and reliably picks up a bottle using a model trained on Eastworlds' data with just $200 of compute.

Eastworlds

58,356 次观看 • 3 个月前

Collecting dexterous humanoid robot data is difficult to scale. That's why Mengda Xu and Han Zhang built DexUMI: a tool for demonstrating how to control a dexterous robot hand, which allows you to quickly collect task data. Co-hosted by Michael Cho - Rbt/Acc and Chris Paxton

Collecting dexterous humanoid robot data is difficult to scale. That's why Mengda Xu and Han Zhang built DexUMI: a tool for demonstrating how to control a dexterous robot hand, which allows you to quickly collect task data. Co-hosted by Michael Cho - Rbt/Acc and Chris Paxton

RoboPapers

26,344 次观看 • 1 年前

.Jason Ma on how Dyna-2 learned to untwist a real water bottle cap out of the box from just 13 minutes of dexterous hand data because the robot hand looks like a human's: "We got a new pair of dexterous hand. Most of our robots in the office have a gripper-like hand, so the gap between that versus human is actually quite large." "We thought, if we have a robot that has hands that look like human, wouldn't the transfer be greater? Right before the release, they were able to collect just 13 minutes of data." "They collected only 13 minutes of data and then post-trained the best Dyna model that trained on one million hours of data. The model is just able to, out of the box, start opening bottle caps on real water bottles." "This is very surprising because typically post-training a robot would take at least several hours of data. For a dexterous hand that's much more complex than a gripper, it would actually be much harder to train. But the experimental results we saw was the opposite. Perhaps because the hand looks more like the human, the transfer was easier." Dyna Robotics

.Jason Ma on how Dyna-2 learned to untwist a real water bottle cap out of the box from just 13 minutes of dexterous hand data because the robot hand looks like a human's: "We got a new pair of dexterous hand. Most of our robots in the office have a gripper-like hand, so the gap between that versus human is actually quite large." "We thought, if we have a robot that has hands that look like human, wouldn't the transfer be greater? Right before the release, they were able to collect just 13 minutes of data." "They collected only 13 minutes of data and then post-trained the best Dyna model that trained on one million hours of data. The model is just able to, out of the box, start opening bottle caps on real water bottles." "This is very surprising because typically post-training a robot would take at least several hours of data. For a dexterous hand that's much more complex than a gripper, it would actually be much harder to train. But the experimental results we saw was the opposite. Perhaps because the hand looks more like the human, the transfer was easier." Dyna Robotics

MTS

13,716 次观看 • 1 个月前

Origami Robotics is building high-DOF robotic hands with in-joint motors and a co-designed data-collection glove to eliminate the embodiment gap by collecting high-quality, real-world data at scale. Congrats on the launch, Quanting Xie and Ryan Xie!

Origami Robotics is building high-DOF robotic hands with in-joint motors and a co-designed data-collection glove to eliminate the embodiment gap by collecting high-quality, real-world data at scale. Congrats on the launch, Quanting Xie and Ryan Xie!

Y Combinator

89,704 次观看 • 6 个月前

What happens when vision🤝 robotics meet? Happy to share our new work on Pretraining Robotic Foundational Models!🔥 ARM4R is an Autoregressive Robotic Model that leverages low-level 4D Representations learned from human video data to yield a better robotic model. Berkeley AI Research😊

What happens when vision🤝 robotics meet? Happy to share our new work on Pretraining Robotic Foundational Models!🔥 ARM4R is an Autoregressive Robotic Model that leverages low-level 4D Representations learned from human video data to yield a better robotic model. Berkeley AI Research😊

Roei Herzig

62,805 次观看 • 1 年前

For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at work.

For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at work.

Satya Nadella

351,122 次观看 • 1 年前

NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

Sawyer Merritt

624,910 次观看 • 8 个月前

Real-world robot data is expensive and slow to collect, creating a major challenge for humanoid development. 🤖 The NVIDIA GR00T N1.6 open vision language action model is pre-trained on a diverse mix of data, including thousands of hours of Stanford Vision and Learning Lab’s BEHAVIOR simulation data, which covers long-horizon everyday manipulation tasks. This diverse training is the key to robust cross-embodiment performance and real-world adaptability. 🌍 Read the blog 🔗

Real-world robot data is expensive and slow to collect, creating a major challenge for humanoid development. 🤖 The NVIDIA GR00T N1.6 open vision language action model is pre-trained on a diverse mix of data, including thousands of hours of Stanford Vision and Learning Lab’s BEHAVIOR simulation data, which covers long-horizon everyday manipulation tasks. This diverse training is the key to robust cross-embodiment performance and real-world adaptability. 🌍 Read the blog 🔗

NVIDIA Robotics

13,535 次观看 • 7 个月前