Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Robots that learn should generalize. Locomotion should show generalization across terrain; manipulation across object categories. Here is a video of “Pick Anything”, demonstrating human like grasps for 66 different objects, ranging in size and shape from air pods to dumbbells to pipe wrenches, using a single policy. The system... show more

Jitendra MALIK

12,897 subscribers

43,147 görüntüleme • 4 gün önce •via X (Twitter)

Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

8 Yorum

Orwell by the Sea profil fotoğrafı

Orwell by the Sea4 gün önce

RL in sim and real?

hosan profil fotoğrafı

hosan4 gün önce

66 objects from AirPods to dumbbells is the right kind of demo: breadth, not one hero task. From the data-collection side, my question is where the grasp diversity came from. Human demos, sim, or the policy itself? That ratio decides what data teams should actually pay for.

GigaCrafter profil fotoğrafı

GigaCrafter4 gün önce

Zero teleoperation data and still human-like grasps on stuff as different as AirPods and pipe wrenches? That's a big deal. Curious what it does with weird geometry, any fun failure cases to share?

Soumil Bhandari profil fotoğrafı

Soumil Bhandari4 gün önce

66 objects from airpods to dumbbells is a crazy range. the airpods are probably the harder one right? small and slippery is way worse than heavy

AI Quanting profil fotoğrafı

AI Quanting4 gün önce

How many of the 66 were never seen in training? The dumbbell and the pipe wrench are a lot more convincing if they're new to the policy.

Jitendra MALIK profil fotoğrafı

Jitendra MALIK4 gün önce

Objects like these were used in training (indeed lot more than these 66). If you want to learn human-like grasps you need to show examples of humans grasping various objects. But once you show "enough" of these, the magic of big data kicks in. Generalization across lighting, position, background clutter is automatic and doesn't need big data.

AI Quanting profil fotoğrafı

AI Quanting4 gün önce

Got it, thanks. Did you hit a rough point where more human grasp videos stopped helping? I'm curious how big "enough" turned out to be.

goodcoffee profil fotoğrafı

goodcoffee4 gün önce

Great experience

Benzer Videolar

Amazon is training humanoids to move boxes. Makes sense! OmniRetarget is a data generation engine that enables complex loco-manipulation for humanoids. It uses offline retargeting from human MoCap datasets and augments data from single demos to produce 8 hours of trajectories that train RL policies in simulation, transferred zero-shot to real robots. Amazon FAR is the primary affiliation for the paper:

Amazon is training humanoids to move boxes. Makes sense! OmniRetarget is a data generation engine that enables complex loco-manipulation for humanoids. It uses offline retargeting from human MoCap datasets and augments data from single demos to produce 8 hours of trajectories that train RL policies in simulation, transferred zero-shot to real robots. Amazon FAR is the primary affiliation for the paper:

The Humanoid Hub

106,447 görüntüleme • 1 yıl önce

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 📄

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

23,580 görüntüleme • 4 ay önce

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.

Mandi Zhao

122,366 görüntüleme • 1 yıl önce

Wouldn't it be great if we could train robots without any teleoperation! In our latest paper, we train robots to mimic a human video of the task by simply matching the object features using RL. We only need one video and under an hour of robot training.

Wouldn't it be great if we could train robots without any teleoperation! In our latest paper, we train robots to mimic a human video of the task by simply matching the object features using RL. We only need one video and under an hour of robot training.

Lerrel Pinto

46,221 görüntüleme • 2 yıl önce

Using computer vision techniques, the robot analyzed each frame of video and was able to identify the different objects and features. In the future, robots will be able to learn human behaviors just by watching videos.

Using computer vision techniques, the robot analyzed each frame of video and was able to identify the different objects and features. In the future, robots will be able to learn human behaviors just by watching videos.

Aakash Gupta

55,274 görüntüleme • 3 yıl önce

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.

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 görüntüleme • 1 yıl önce

Introducing Open-TeleVision: with Fully Autonomous policy video👇. We can conduct a long-horizon task with inserting 12 cans nonstop without any interruptions. We offer: 🤖 Highly precise and smooth bimanual manipulation. 📺 Active egocentric vision (with a moving neck) feedback. It is achieved by imitation learning from teleoperation: We propose a VR-based REAL-TIME teleoperation that streams the stereo video observation from the robot camera to the VR device. The robot neck moves as the human head moves, the robot hands move as the human hands move, offering the operator an intuitive experience as the human herself becomes the robot. The devils are all in the details, and how to implement things right: ✅How to perform IK/retargeting for smooth and precise control. ✅How to do all these and also stream stereo video without no latency, all in real time. We released our code here: Active head hardware design: 1/n

Introducing Open-TeleVision: with Fully Autonomous policy video👇. We can conduct a long-horizon task with inserting 12 cans nonstop without any interruptions. We offer: 🤖 Highly precise and smooth bimanual manipulation. 📺 Active egocentric vision (with a moving neck) feedback. It is achieved by imitation learning from teleoperation: We propose a VR-based REAL-TIME teleoperation that streams the stereo video observation from the robot camera to the VR device. The robot neck moves as the human head moves, the robot hands move as the human hands move, offering the operator an intuitive experience as the human herself becomes the robot. The devils are all in the details, and how to implement things right: ✅How to perform IK/retargeting for smooth and precise control. ✅How to do all these and also stream stereo video without no latency, all in real time. We released our code here: Active head hardware design: 1/n

Xiaolong Wang

25,744 görüntüleme • 2 yıl önce

It’s been a massive week for embodied AI foundation models: the pace of this field is truly staggering. Throwing it back to the SimToolReal work by Kushal and Tyler Lum (Cornell & Stanford labs). In February, they achieved zero-shot tool manipulation across 24 tasks using a single RL policy and a robotic arm fitted with the SharpaWave hand. Watching the robot nail these high-speed in-hand rotations is incredible. The precision is especially impressive when you consider the policy was never trained on these specific objects or tasks. This is what solving the manipulation bottleneck looks like. ⚡️

It’s been a massive week for embodied AI foundation models: the pace of this field is truly staggering. Throwing it back to the SimToolReal work by Kushal and Tyler Lum (Cornell & Stanford labs). In February, they achieved zero-shot tool manipulation across 24 tasks using a single RL policy and a robotic arm fitted with the SharpaWave hand. Watching the robot nail these high-speed in-hand rotations is incredible. The precision is especially impressive when you consider the policy was never trained on these specific objects or tasks. This is what solving the manipulation bottleneck looks like. ⚡️

Sharpa

868,646 görüntüleme • 6 ay önce

Qwen-VLA feels like one of the first real robotics foundation models. A single system trained across robot manipulation, navigation, egocentric human video, simulation, and vision-language reasoning instead of isolated robot policies.

Qwen-VLA feels like one of the first real robotics foundation models. A single system trained across robot manipulation, navigation, egocentric human video, simulation, and vision-language reasoning instead of isolated robot policies.

Robots Digest 🤖

14,722 görüntüleme • 5 ay önce

Sim2Real RL for Vision-Based Dexterous Manipulation on Humanoids TLDR - we train a humanoid robot with two multifingered hands to perform a range of dexterous manipulation tasks robust generalization and high performance without human demonstration :D

Sim2Real RL for Vision-Based Dexterous Manipulation on Humanoids TLDR - we train a humanoid robot with two multifingered hands to perform a range of dexterous manipulation tasks robust generalization and high performance without human demonstration :D

Toru

49,892 görüntüleme • 1 yıl önce

Teaching robots from human video is an important part of overcoming the “data gap” in robotics, but many of the details still need to be worked out. Homanga Bharadhwaj tells us about two recent research papers, Gen2Act and Spider, which go over different aspects of the problem: Gen2Act uses generative video models to create a reference of how a task should be performed given a language prompt; then, it uses a multi-purpose policy that can “translate” from human video to robot motion. However, Gen2Act has its limitations, in particular when it comes to dexterous, contact-rich tasks. That’s where SPIDER comes in: it uses human data together with simulation to train policies across many different humanoid hands and datasets. Also of note is that this is our first episode with our new rotating co-host, Jiafei Duan. To learn more, watch Episode #57 of RoboPapers now, with Chris Paxton and Jiafei Duan!

Teaching robots from human video is an important part of overcoming the “data gap” in robotics, but many of the details still need to be worked out. Homanga Bharadhwaj tells us about two recent research papers, Gen2Act and Spider, which go over different aspects of the problem: Gen2Act uses generative video models to create a reference of how a task should be performed given a language prompt; then, it uses a multi-purpose policy that can “translate” from human video to robot motion. However, Gen2Act has its limitations, in particular when it comes to dexterous, contact-rich tasks. That’s where SPIDER comes in: it uses human data together with simulation to train policies across many different humanoid hands and datasets. Also of note is that this is our first episode with our new rotating co-host, Jiafei Duan. To learn more, watch Episode #57 of RoboPapers now, with Chris Paxton and Jiafei Duan!

RoboPapers

24,286 görüntüleme • 10 ay önce

Researchers at RAI Institute developed the Koala gripper to help robots handle tools and objects more naturally. The system includes a handheld version for humans and a motorized version for robots, both using the same design and sensors. By learning from human demonstrations recorded with cameras and force sensors, robots can repeat the same tasks using the Koala gripper.

Researchers at RAI Institute developed the Koala gripper to help robots handle tools and objects more naturally. The system includes a handheld version for humans and a motorized version for robots, both using the same design and sensors. By learning from human demonstrations recorded with cameras and force sensors, robots can repeat the same tasks using the Koala gripper.

Space and Technology

22,174 görüntüleme • 5 ay önce

What if we're approaching robotic manipulation all wrong? Ilya Sutskever makes a fascinating point here: Current approaches throw massive amounts of data at the problem — millions of simulation steps, huge compute. And still, robots can't match human dexterity. Real-world learning of new skills? "Very out of reach." Meanwhile, humans pick up manipulation tasks almost instantly. Our dexterity is unmatched. And we do it with remarkable robustness — no reward shaping, no curriculum design, no training instability. Why the gap? Evolution. 500M years of optimisation for locomotion, vision, and manipulation. For these ancient sensorimotor tasks, maybe brute-force data isn't the answer. Maybe we should be copying the biological algorithms that already solved it. CC: Dwarkesh Patel

What if we're approaching robotic manipulation all wrong? Ilya Sutskever makes a fascinating point here: Current approaches throw massive amounts of data at the problem — millions of simulation steps, huge compute. And still, robots can't match human dexterity. Real-world learning of new skills? "Very out of reach." Meanwhile, humans pick up manipulation tasks almost instantly. Our dexterity is unmatched. And we do it with remarkable robustness — no reward shaping, no curriculum design, no training instability. Why the gap? Evolution. 500M years of optimisation for locomotion, vision, and manipulation. For these ancient sensorimotor tasks, maybe brute-force data isn't the answer. Maybe we should be copying the biological algorithms that already solved it. CC: Dwarkesh Patel

Jack 🤖

31,262 görüntüleme • 11 ay önce

Introducing HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction. We built a multi-camera system and a semi-automatic method for annotating the shape and pose of hands and objects Project page:

Introducing HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction. We built a multi-camera system and a semi-automatic method for annotating the shape and pose of hands and objects Project page:

Yu Xiang

57,604 görüntüleme • 1 yıl önce

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!

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

27,360 görüntüleme • 6 ay önce

We believe we’re the first robotics company to demonstrate a robot peeling an apple with dual dexterous human-like hands. This breakthrough closes a key gap in robotics, achieving bimanual, contact-rich manipulation and moving far beyond the limits of simple grippers. 🧵↓ Today’s AI models (VLMs) are excellent at perception but struggle with action. Controlling high-degree-of-freedom hands for tasks like this is incredibly complex, and precise finger-level teleoperation is nearly impossible for humans. Our first step was a shared-autonomy system: rather than controlling every finger, the operator triggers pre-learned skills like a “rotate apple or tennis ball” primitive via a keyboard press or pedal. This makes scalable data collection and RL training possible. How does the AI manage this? We created "MoDE-VLA" (Mixture of Dexterous Experts). It fuses vision, language, force, and touch data by using a team of specialist "experts," making control in high-dimensional spaces stable and effective. The combination of these two innovations allows for seamless, contact-rich manipulation. The human provides high-level guidance, and the robot executes the complex in-hand coordination required. This work paves the way for robots that can safely handle delicate tasks in human environments. Want the full technical details? 📄 Read the full research paper: Visit us at NVIDIA GTC Booth #1838, Hall 3 to learn more! #Robotics #AI #DexterousManipulation #VLA #NVIDIAGTC Nancy Villicaña NVIDIA GTC

We believe we’re the first robotics company to demonstrate a robot peeling an apple with dual dexterous human-like hands. This breakthrough closes a key gap in robotics, achieving bimanual, contact-rich manipulation and moving far beyond the limits of simple grippers. 🧵↓ Today’s AI models (VLMs) are excellent at perception but struggle with action. Controlling high-degree-of-freedom hands for tasks like this is incredibly complex, and precise finger-level teleoperation is nearly impossible for humans. Our first step was a shared-autonomy system: rather than controlling every finger, the operator triggers pre-learned skills like a “rotate apple or tennis ball” primitive via a keyboard press or pedal. This makes scalable data collection and RL training possible. How does the AI manage this? We created "MoDE-VLA" (Mixture of Dexterous Experts). It fuses vision, language, force, and touch data by using a team of specialist "experts," making control in high-dimensional spaces stable and effective. The combination of these two innovations allows for seamless, contact-rich manipulation. The human provides high-level guidance, and the robot executes the complex in-hand coordination required. This work paves the way for robots that can safely handle delicate tasks in human environments. Want the full technical details? 📄 Read the full research paper: Visit us at NVIDIA GTC Booth #1838, Hall 3 to learn more! #Robotics #AI #DexterousManipulation #VLA #NVIDIAGTC Nancy Villicaña NVIDIA GTC

Sharpa

20,734 görüntüleme • 8 ay önce

WUJI is showcasing its dexterous robotic hands at IROS 2026, demonstrating how human hand movements can control a robot through glove-based teleoperation. Its WUJI Hand 2 can also perform precise tasks like pen spinning using a policy trained in simulation and transferred to a real robot. These demonstrations highlight advances in robotic finger coordination, dexterity, and fine manipulation.

WUJI is showcasing its dexterous robotic hands at IROS 2026, demonstrating how human hand movements can control a robot through glove-based teleoperation. Its WUJI Hand 2 can also perform precise tasks like pen spinning using a policy trained in simulation and transferred to a real robot. These demonstrations highlight advances in robotic finger coordination, dexterity, and fine manipulation.

Space and Technology

12,949 görüntüleme • 1 ay önce

📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap data can be transferred to Inspire, Shadow, Xhand, Allegro, and Mano. With ManipTrans, we can scale up dex manip data greatly with minimal effort. For more details, please check our -webpage: -code: -huggingface:

📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap data can be transferred to Inspire, Shadow, Xhand, Allegro, and Mano. With ManipTrans, we can scale up dex manip data greatly with minimal effort. For more details, please check our -webpage: -code: -huggingface:

Siyuan Huang

20,983 görüntüleme • 1 yıl önce

Bob McGrew, Former Research Chief at OpenAI: "Manipulation is the hard problem we need to solve to make humanoid robots useful, not locomotion. One video of a humanoid slowly performing tasks with its hands is worth a dozen clips of choreographed routines."

Bob McGrew, Former Research Chief at OpenAI: "Manipulation is the hard problem we need to solve to make humanoid robots useful, not locomotion. One video of a humanoid slowly performing tasks with its hands is worth a dozen clips of choreographed routines."

The Humanoid Hub

95,312 görüntüleme • 1 yıl önce