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I've been working on deformable object manipulation since my PhD. It was totally a nightmare years ago and my PhD advisor was telling me not to work on it for my own good. Today, at ByteDance Seed, we are dropping GR-RL, a new VLA+RL system that manages long-horizon precise... dexterous manipulation of deformable objects. This is probably the first real-world RL system to make a robot: ✅ Lace up your shoes end to end ✅ Hit millimeter tolerance repeatedly ✅ Recover from mistakes (See video!) ✅ And complete continuous shoelace threading on a real bimanual platform 📈 Success rate: ↑ from 45.7% → 83.3% Yes, robots can now actually do this. Project page: ArXiv:show more

Xiao Ma

1,766 subscribers

110,653 次观看 • 10 个月前 •via X (Twitter)

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45 条评论

Xiao Ma 的头像

Xiao Ma10 个月前

Why “shoelace threading” matters 🤔 This task is probably one of the most challenging household robotics tasks in terms of precision: 💥 Soft-body chaos – laces deform every frame 💥 Millimeter precision – 1–2 mm slip = total failure 💥 Long-horizon manipulation – hundreds of steps where slightest drift compounds. 💥 Requires recovery – humans retry; IL models freeze It's the perfect torture test for VLA precision. Even our previous model, GR-3 — already trained on massive robot trajectories + human teleop demos + public image-text corpora — failed to get reliable policies here. So we found the bottleneck.

Xiao Ma 的头像

Xiao Ma10 个月前

Two killers of imitation learning (IL): (1) Human demos are NOT optimal Humans hesitate, retry, fix mistakes mid-trajectory. IL blindly copies ALL of it — including the bad parts. (2) Training vs Deployment Misalignment VLA models output actions. To prevent jitter, robots execute post-processed versions (RHC, smoothing, ensembling). Your “predicted action” ≠ your “executed action.” On millimeter tasks, this mismatch is fatal. So IL alone was a dead end.

Xiao Ma 的头像

Xiao Ma10 个月前

The Idea: If imitation is broken, then: Let the robot learn from its own experience. GR-RL = ⭐️ Offline RL (data filtering) ⭐️ Symmetry augmentation ⭐️ Online closed-loop Real-World Reinforcement Learning All on top of a single VLA foundation model. Just RGB, proprioception, and language instructions.

Xiao Ma 的头像

Xiao Ma10 个月前

Offline Stage — Filter the human flaws We train a Critic Transformer via distributional RL: ⭐️ Detects “value drops” when the operator hesitates or messes up ⭐️ Slices every trajectory into high-value vs low-value segments ⭐️ Retains only the cleanest expert behavior Effect: GR-3 45.7% → 61.6% success just from removing “bad micro-behaviors.”

Xiao Ma 的头像

Xiao Ma10 个月前

Morphological Symmetry Augmentation Our bi-manual robot is left–right symmetric. So we mirror EVERYTHING: 🚀 RGB 🚀 Proprioception 🚀 Actions 🚀 Language Instructions Data size doubles. Spatial reasoning robustness skyrockets. → 72.7% success.

Xiao Ma 的头像

Xiao Ma10 个月前

Online Stage — Real-World Steering RL Now the robot learns ON THE PHYSICAL PLATFORM. But direct exploration in joint space causes dangerous jitter and is inefficient — you need millimeter accuracy. So GR-RL explores in the latent noise space: A tiny 51.5M-param noise-predictor nudges the flow model’s latent action distribution toward high-value regions. We also introduce a dual-buffer mechanism: 🚀 Off-policy buffer (old rollouts) for the critic warm-up 🚀 On-policy buffer (last 2 checkpoints) for stable improvement 🚀 1:1 sampling stabilizes RL and prevents catastrophic drift. Just 150 online rollouts → +10% performance boost.

Xiao Ma 的头像

Xiao Ma10 个月前

The Result: Final performance: 83.3% success over continuous shoelace threading. The surprising part? GR-RL learns to: 🔥 retry when the lace slips 🔥 reposition the lace when the initial pose is bad 🔥 "self-correct" mid-task instead of freezing This is the behavior you EXPECT from embodied intelligence — not just mimicry, but competence.

Xiao Ma 的头像

Xiao Ma10 个月前

GR-RL proves something: IL is inherently limited, and we can do things previously thought impossible by purely visuo-motor control simply making it RL. The future direction is clear: distill RL-enhanced behavior back into the foundation VLA, forming a self-improving, ever-growing loop. Kudos to the wonderful team! @yunfeili98, @simanbulk, @yu_cui123, Zhongren Cui, Zhigang Han, @litchiyoyo, @KT892790499, @YuxiaoLiu4, Hao Niu, Wanli Peng, Jingchao Qiao, Zeyu Ren, Haixin Shi, @ZhiSu22, Jiawen Tian, Yuyang Xiao, Shenyu Zhang, Liwei Zheng

Perry Jia 的头像

Perry Jia10 个月前

Wow! I love how it naturally pulls the shoe closer so that it can complete the task easier. Awesome work!

Xiao Ma 的头像

Xiao Ma10 个月前

haha, thanks Perry! btw congrats on your recent release on ACT-1 and Memo! Great work! I really like the design and it's amazing how much it has achieved with pure UMI data.

🄱🄻🄸🅃🅉 🔸 的头像

🄱🄻🄸🅃🅉 🔸10 个月前

This is brilliant Ma

Adam 的头像

Adam10 个月前

incredible work, can't wait to see more in the future.

Daniel Domingos 的头像

Daniel Domingos10 个月前

Congrats!

MAX ONBOARDER ⭕ 的头像

MAX ONBOARDER ⭕10 个月前

@Scobleizer This is amazing

Bingyi Kang 的头像

Bingyi Kang10 个月前

Great team! Great Work! Can not wait to see your future project! Congrats @yunfeili98 @yusufma555

Salman 的头像

Salman10 个月前

This is incredible work. Kudos for seeing this work through for so many years, seems like it’s paying off!

Robert 的头像

Robert10 个月前

Can you implement an aspect of leaning against the object that you're working on? This is what humans do when we tie our shoe, we don't float in space. This means that you don't have to have accurate control, you know that if you rest your finger against the shoe, then the string will be in the right place.

Xiao Ma 的头像

Xiao Ma10 个月前

Hey Robert, this is a perfect point. Our model actually sometimes does it. During the offline data collection stage, most of the human demonstrators love to "float" because it is easier for them to control the robot when they became very good at it. However, this causes the issue you mentioned. But there were indeed some samples that the demonstrators love to lean on the shoes. As a result, our VLA model will demonstrate multi behavior modes, including the one you mentioned. But it is just less often, due to the initial data distribution was dominated by the other behavior mode.

Robert 的头像

Robert10 个月前

Awesome, will this methodology ultimately require the robot to be trained on every task that it carries out or do you have some requirement for a "general" intelligence that can carry our arbitrary tasks? I know this is easier said than done, but interested in your opinion as I have no experience with robotics.

Yu Xiang 的头像

Yu Xiang10 个月前

This task seems very difficult. Can the policy deal with different shoes?

Xiao Ma 的头像

Xiao Ma10 个月前

thanks! Currently we tested on three different colored shoes. However, some other shoe designs are extremely difficult to thread, even for human demonstrators. There is much more we can do in the future :)

sam 的头像

sam10 个月前

very impressive :)

Porters Reserve 的头像

Porters Reserve10 个月前

😁 this why a professor is just person who memorized a book to tell you to read the same book. Only real world grit and experimentation is the path to success. Great Job now level up send your gear to the reserve to see what really breaks.

Rxct Ftvy 的头像

Rxct Ftvy10 个月前

Really really cool

Rxct Ftvy 的头像

Rxct Ftvy10 个月前

Very cool

Offend_no_one 的头像

Offend_no_one10 个月前

Wow, Yusuf, that’s incredible progress from ByteDance!

Younggyo Seo 的头像

Younggyo Seo10 个月前

Cool, congrats on the release!!

Xiao Ma 的头像

Xiao Ma10 个月前

Thank you Younggyo!

Griffin 的头像

Griffin10 个月前

Incredibly impressive, well done!

Fanf666 的头像

Fanf66610 个月前

Can you clarify 1) the number of examples to reach your quoted accuracy 2) how many distinct tasks can the model remember and how does it select them

Fanf666 的头像

Fanf66610 个月前

Here is prior art shoelace tyeing What matter is to advance few shot, max task number and length.

Roméo 的头像

Roméo10 个月前

@grok analyse et explique ce qui se passe dans cette vidéo

czr 的头像

czr10 个月前

Cool ！

尚月的头像

尚月10 个月前

还有哪些常见的日常活动对机器人而言与系鞋带平级或者更复杂？

Dan Wu 的头像

Dan Wu10 个月前

So sick! Curious what cameras are mounted onto the end effectors?

Xiao Ma 的头像

Xiao Ma10 个月前

thanks! We have discussed our hardware setups in the section 4 of our paper. It is a realsense D405 camera.

Muhammad Umar 🇺🇸 的头像

Muhammad Umar 🇺🇸10 个月前

It's just a matter of time we'll solve dexterity. This is just unreal.

Commentary Yi He 的头像

Commentary Yi He9 个月前

Amazing project! 👏 Message me directly

Commentary Yi He 的头像

Commentary Yi He9 个月前

Awesome 👏 Message me directly

Reza Sayar 的头像

Reza Sayar10 个月前

this is awesome! 🔥 congrats! 👏🏼 i noticed even though you had RGB-D cameras, you only kept the RGB for training and inference. did you find that depth data won't help here much?

Laughless 的头像

Laughless10 个月前

This is more impressive than dancing or kung fu. Do you think it could tie a neck tie without choking a person? That's one of my benchmarks for when robots are ready for home use.

Raul Verdusco 的头像

Raul Verdusco10 个月前

Incredible progress — deformable object manipulation has always been one of the toughest challenges in robotics, and seeing real-world RL push success rates this high is remarkable. Huge step forward for dexterous autonomy. 🤖🧠 #Robotics #ReinforcementLearning

Golding 的头像

Golding10 个月前

This is pretty cool man. I'd love to learn more about your research and ByteDance Seed. Can you open your DM's? let's chat!

Farhan Azad Shuvra 的头像

Farhan Azad Shuvra10 个月前

Impressive Stuff!!

X Æ A-12 的头像

X Æ A-1210 个月前

you are the best 😍🧡💙💚 !

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Dylan Small

15,054 次观看 • 3 个月前

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

204,737 次观看 • 5 个月前

Jazmyn Nyx on why she left WWE. “I truthfully can say I was not expecting to leave. I wholeheartedly felt like this was going to be my life. I was ready to put in the work and see where my journey took me.” “For me it was like, after my three years and being on TV for two years, I expected there to be some sort of compensation just for the work that I put in. And it didn’t end up that way, and that’s okay. It was just okay, I can’t make a living on this.”

Jazmyn Nyx on why she left WWE. “I truthfully can say I was not expecting to leave. I wholeheartedly felt like this was going to be my life. I was ready to put in the work and see where my journey took me.” “For me it was like, after my three years and being on TV for two years, I expected there to be some sort of compensation just for the work that I put in. And it didn’t end up that way, and that’s okay. It was just okay, I can’t make a living on this.”

wrestlescripts📝

349,751 次观看 • 3 个月前

What if a dexterous robot could learn a 20+ step chemistry experiment without a single on-robot training demo? Meet TwinDEX: a pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment. The twinned design shares identical kinematics, contact surfaces, visual appearance, and sensors across collection and deployment — keeping observations and actions aligned end to end. Trained from scratch on only a few hundred wearable demonstrations - with zero on-robot training or intervention data - TwinDEX completed a standardized chemistry experiment involving tool switches, fine force control, and bimanual coordination. Robot-free data showed comparable learning efficiency on the multi-task evaluation, TwinDEX delivered 5.3 times effective throughput than on-robot teleoperation. TwinDEX demonstrates that high-quality robot-free data can fully substitute for on-robot teleoperation data on challenging dexterous tasks — removing the dependency on real-robot hardware that has been the central bottleneck to scaling dexterous manipulation data. This was the proof-it phase. Now comes scale: what emerges at tens of thousands, or millions, of episodes? Watch the demo and read the technical blog: #TwinDEX #Robotics #EmbodiedAI #DexterousManipulation

What if a dexterous robot could learn a 20+ step chemistry experiment without a single on-robot training demo? Meet TwinDEX: a pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment. The twinned design shares identical kinematics, contact surfaces, visual appearance, and sensors across collection and deployment — keeping observations and actions aligned end to end. Trained from scratch on only a few hundred wearable demonstrations - with zero on-robot training or intervention data - TwinDEX completed a standardized chemistry experiment involving tool switches, fine force control, and bimanual coordination. Robot-free data showed comparable learning efficiency on the multi-task evaluation, TwinDEX delivered 5.3 times effective throughput than on-robot teleoperation. TwinDEX demonstrates that high-quality robot-free data can fully substitute for on-robot teleoperation data on challenging dexterous tasks — removing the dependency on real-robot hardware that has been the central bottleneck to scaling dexterous manipulation data. This was the proof-it phase. Now comes scale: what emerges at tens of thousands, or millions, of episodes? Watch the demo and read the technical blog: #TwinDEX #Robotics #EmbodiedAI #DexterousManipulation

X Square Robot

1,346,470 次观看 • 1 个月前

Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

Léo

32,513 次观看 • 2 个月前

You can now operate robots by just thinking about it. With your brain signals. WOW. This robot system from Stanford has so much sci-fi vibe and wild implications that I don't even know where to start. NOIR decodes the EEG signal from your head into a library of robot skills. It is demonstrated on 20 household activities, such as cooking Sukiyaki, ironing clothes, grating cheese, playing Tic-Tac-Toe, and even petting a robot dog! NOIR learns to predict your intended goals in advance, so that your thinking efforts (literally) can be reduced to a minimum. It works with both adults and children as young as five years old. Imagine scaling up this system to Neuralink + Humanoid robots like Tesla Optimus in the future! There's now a decent probability that we can see the movie Avatar become a reality in our lifetime. Website: Paper: This work is from Stanford Vision Lab, my PhD home. The project is led by Ruohan Zhang and advised by Fei-Fei Li Jiajun Wu. Congrats on the phenomenal idea!

Jim Fan

378,451 次观看 • 2 年前

🇺🇸 YOUNG AMERICAN WORKER: OUR PAYCHECKS DON'T COVER OUR BILLS "I work 5 days a week, 40 hours a week. I do not make enough to live on my own. I do not make enough to pay rent, water, electric and eat, all by myself. 20 years ago, when you first started, you could live on your own. I've been working my tail end off just to barely make it by. I don't want to do that for the rest of my life." Source: @motivationhoused on Instagram

🇺🇸 YOUNG AMERICAN WORKER: OUR PAYCHECKS DON'T COVER OUR BILLS "I work 5 days a week, 40 hours a week. I do not make enough to live on my own. I do not make enough to pay rent, water, electric and eat, all by myself. 20 years ago, when you first started, you could live on your own. I've been working my tail end off just to barely make it by. I don't want to do that for the rest of my life." Source: @motivationhoused on Instagram

Mario Nawfal

317,230 次观看 • 2 年前