正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

we open sourced the code to transform human videos into robot trajectories, so you can train robots with your hands 👐🏻 we used it in our recent paper R+X: Retrieval and Execution from Everyday Human Videos (ICRA 2025 🇺🇸) link and details in thread 🧵

Norman Di Palo

1,518 subscribers

10,578 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

How can we leverage diverse human videos to improve robot manipulation? Excited to introduce EgoVLA — a Vision-Language-Action model trained on egocentric human videos by explicitly modeling wrist & hand motion. We build a shared action space between humans and robots, enabling seamless transfer. With some robot demos, EgoVLA becomes a powerful, generalizable robot policy.

How can we leverage diverse human videos to improve robot manipulation? Excited to introduce EgoVLA — a Vision-Language-Action model trained on egocentric human videos by explicitly modeling wrist & hand motion. We build a shared action space between humans and robots, enabling seamless transfer. With some robot demos, EgoVLA becomes a powerful, generalizable robot policy.

Ruihan Yang

58,712 次观看 • 1 年前

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.

Physical Intelligence

120,595 次观看 • 7 个月前

Grasps are one of the primary ways in which we interact with and shape our environments. How can we faithfully capture human grasps with details such as hand/object shape and contact points? At #CVPR2026, we present MANUS, a method to accurately reconstruct grasps and contacts. 🧵

Grasps are one of the primary ways in which we interact with and shape our environments. How can we faithfully capture human grasps with details such as hand/object shape and contact points? At #CVPR2026, we present MANUS, a method to accurately reconstruct grasps and contacts. 🧵

Srinath Sridhar

10,779 次观看 • 2 年前

X just open-sourced their algorithm code, and now you know how to position your content for max visibility. 10 keys to make your posts blow up, in this thread 🧵 1. Video is king, gets a 1.8x boost 👇

X just open-sourced their algorithm code, and now you know how to position your content for max visibility. 10 keys to make your posts blow up, in this thread 🧵 1. Video is king, gets a 1.8x boost 👇

TechHalla

15,802 次观看 • 10 个月前

Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.

Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.

MrNeRF

24,729 次观看 • 1 年前

Don't you have a haptic chair for VR gaming? Don't worry, your friendly humanoid robot can help you with that! In a recent research paper from the University of Colorado, researchers experimented with using a humanoid robot to literally grab your chair and move it during VR racing sessions to make the game more realistic. And from the users' feedback, it seemed to work. I guess they took inspiration from those viral videos, where in some low-budget arcades, the owner literally moved the station with his own hands. I can't wait for the fail videos caused by this technology... Full video is available here: #VirtualReality #VR #technology

Don't you have a haptic chair for VR gaming? Don't worry, your friendly humanoid robot can help you with that! In a recent research paper from the University of Colorado, researchers experimented with using a humanoid robot to literally grab your chair and move it during VR racing sessions to make the game more realistic. And from the users' feedback, it seemed to work. I guess they took inspiration from those viral videos, where in some low-budget arcades, the owner literally moved the station with his own hands. I can't wait for the fail videos caused by this technology... Full video is available here: #VirtualReality #VR #technology

TonyVT SkarredGhost

80,441 次观看 • 3 个月前

A FAQ thread about Horiza Horiza | Beyond DeFi We always get questions from the community, and today we address them in a thread Note that you can ask your questions in the comments! 🧵❓

A FAQ thread about Horiza Horiza | Beyond DeFi We always get questions from the community, and today we address them in a thread Note that you can ask your questions in the comments! 🧵❓

Horiza | Beyond DeFi

14,094 次观看 • 2 年前

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more details

Oier Mees

12,277 次观看 • 1 个月前

Proud to announce Dobb·E: the next step in home robot system that I was working on for the past 3 years. We have visited 10 homes, learned 100+ tasks, and we are just getting started! And we fully open-sourced it all, hardware, models, and software: 🧵

Proud to announce Dobb·E: the next step in home robot system that I was working on for the past 3 years. We have visited 10 homes, learned 100+ tasks, and we are just getting started! And we fully open-sourced it all, hardware, models, and software: 🧵

Mahi Shafiullah 🏠🤖

164,452 次观看 • 2 年前

How can we more effectively leverage robot data from different embodiments for skill transfer? Excited to share that our new work, RoVi-Aug, has been accepted to Conference on Robot Learning as an oral paper！ WIth RoVi-Aug, you can augment an existing robot dataset into a different robot and different viewpoints. A policy trained on the augmented dataset can zero-shot deploy on the unseen target robot with significantly different camera angles! 🧵👇 🔗 Check out our paper:

How can we more effectively leverage robot data from different embodiments for skill transfer? Excited to share that our new work, RoVi-Aug, has been accepted to Conference on Robot Learning as an oral paper！ WIth RoVi-Aug, you can augment an existing robot dataset into a different robot and different viewpoints. A policy trained on the augmented dataset can zero-shot deploy on the unseen target robot with significantly different camera angles! 🧵👇 🔗 Check out our paper:

Chenfeng_X

28,304 次观看 • 1 年前

HIP MOBILITY - We can all benefit from some added attention to the hips. If you are unsure of where to start… Start with this thread 🧵 Hip swivels, use your hands to keep your posture upright and alternate legs going into internal rotation.

HIP MOBILITY - We can all benefit from some added attention to the hips. If you are unsure of where to start… Start with this thread 🧵 Hip swivels, use your hands to keep your posture upright and alternate legs going into internal rotation.

Vernon Griffith, CSCS*D

101,285 次观看 • 1 年前

Tesla engineers on the next-gen Optimus V3 hand: "As we go into gen-3 and into mass production, we are getting very close to human functionality and form factor. It won't even look like a robot. It will look like a human in a superhero suit. It will be something revolutionary."

Tesla engineers on the next-gen Optimus V3 hand: "As we go into gen-3 and into mass production, we are getting very close to human functionality and form factor. It won't even look like a robot. It will look like a human in a superhero suit. It will be something revolutionary."

Sawyer Merritt

513,260 次观看 • 4 个月前

Eight artists. Eight Stories. Procreate presents Human Made. We explore human creativity through intimate interviews with artists from across the globe. Human Made kicks off on 19 November on our YouTube channel. With two artist interviews dropping weekly, make sure you subscribe to catch each new episode and other fresh #Procreate content ✨ Use #ProcreateArtist to your own human made artworks so we can discover them!

Eight artists. Eight Stories. Procreate presents Human Made. We explore human creativity through intimate interviews with artists from across the globe. Human Made kicks off on 19 November on our YouTube channel. With two artist interviews dropping weekly, make sure you subscribe to catch each new episode and other fresh #Procreate content ✨ Use #ProcreateArtist to your own human made artworks so we can discover them!

Procreate

83,928 次观看 • 1 年前

today we launched 🪴Boosts v2🌴 in Arc. a collection of simple tools that let you edit the internet and make it your own (mostly without code!) a thread of my favorite artifacts from this project: early prototypes, fun design details, and deleted scenes 🧵:

today we launched 🪴Boosts v2🌴 in Arc. a collection of simple tools that let you edit the internet and make it your own (mostly without code!) a thread of my favorite artifacts from this project: early prototypes, fun design details, and deleted scenes 🧵:

nate parrott

153,287 次观看 • 3 年前

We are hiring FT SW/ML and Mechanical roles at Human Computer Lab Robots aren't just for laundry and dishes in homes. LeLamp has a unique constraint today: it doesn't move through space or manipulate objects. That limitation pushes us to think deeply about how LeLamp can understand human intent and translate it into trajectories that make it feel alive. This makes us singularly focused on defining a new benchmark for interacting with technology — one that feels joyful and makes us more aware of our relationship to it.

We are hiring FT SW/ML and Mechanical roles at Human Computer Lab Robots aren't just for laundry and dishes in homes. LeLamp has a unique constraint today: it doesn't move through space or manipulate objects. That limitation pushes us to think deeply about how LeLamp can understand human intent and translate it into trajectories that make it feel alive. This makes us singularly focused on defining a new benchmark for interacting with technology — one that feels joyful and makes us more aware of our relationship to it.

Shahvir Sarkary

31,550 次观看 • 3 个月前

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 次观看 • 5 个月前

Many of you asked for code & weights for π₀, we are happy to announce that we are releasing π₀ and pre-trained checkpoints in our new openpi repository! We tested the model on a few public robots, and we include code for you to fine-tune it yourself.

Many of you asked for code & weights for π₀, we are happy to announce that we are releasing π₀ and pre-trained checkpoints in our new openpi repository! We tested the model on a few public robots, and we include code for you to fine-tune it yourself.

Physical Intelligence

441,382 次观看 • 1 年前

(1/2) MonoNPHM will be presented as a #CVPR2024 Highlight! Our Neural Parametric Head Model parametrizes both geometry and appearance. With the learned model, we can then 3D reconstruct and track human heads from images or videos.

(1/2) MonoNPHM will be presented as a #CVPR2024 Highlight! Our Neural Parametric Head Model parametrizes both geometry and appearance. With the learned model, we can then 3D reconstruct and track human heads from images or videos.

Matthias Niessner

17,209 次观看 • 2 年前

This is #AlphaSOUTH and we are so excited to welcome everyone in your new home 🌈 OUR DOORS WILL OPEN 1PM-3AM JULY 2023 THURSDAY! Unlimited stay all-day-everyday with FREE DRINKS! Join us as we welcome everyone from the south! ♥️ THIS IS IT!!!

This is #AlphaSOUTH and we are so excited to welcome everyone in your new home 🌈 OUR DOORS WILL OPEN 1PM-3AM JULY 2023 THURSDAY! Unlimited stay all-day-everyday with FREE DRINKS! Join us as we welcome everyone from the south! ♥️ THIS IS IT!!!

Alpha Massage

273,603 次观看 • 3 年前

Chat is here! Now you can message your friends in app when you’re running late and have more fun with your convos when you have more time. With the chat you can: - Drag and drop text, stickers, drawings, videos and more - Start convos directly from the map - See (and even feel!) when friends are in the chat at the same time as you - Screenshot to export timelapses of your creations Share your best convos in this thread, we want to see them!

Chat is here! Now you can message your friends in app when you’re running late and have more fun with your convos when you have more time. With the chat you can: - Drag and drop text, stickers, drawings, videos and more - Start convos directly from the map - See (and even feel!) when friends are in the chat at the same time as you - Screenshot to export timelapses of your creations Share your best convos in this thread, we want to see them!

Bump app

57,734 次观看 • 1 年前