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Human Archive builds datasets to model sensorimotor intelligence for robotics and world models. HA-Multi is the largest dataset of manual labor tasks aligning egocentric RGB, stereo depth (active IR), tactile gloves, body IMUs, and wrist cameras. Congrats on the launch, Raj Patel, Shloke Patel, Samay Maini, and Rushil Agarwal!

Y Combinator

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110,791 görüntüleme • 6 ay önce •via X (Twitter)

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Runtime profil fotoğrafı

Runtime6 ay önce

Onwards and upwards!

Panda Hodler (YC Intern) profil fotoğrafı

Panda Hodler (YC Intern)6 ay önce

This seems to equip robots with "human-level tactile and visual nerves." The team's approach is very interesting i think😆

WildPinesAI profil fotoğrafı

WildPinesAI6 ay önce

tactile force maps aligned with vision is the missing dataset every robotics foundation model is starving for

Samay Maini profil fotoğrafı

Samay Maini6 ay önce

let’s archive the world for embodied intelligence!

Kai profil fotoğrafı

Kai6 ay önce

This stack is wild, egocentric video plus tactile plus IMU in one dataset is exactly what makes sim to real less brittle. Fine tuning policies from this should beat pure vision only sets.

Maahir Sachdev profil fotoğrafı

Maahir Sachdev6 ay önce

congrats champ

Kyriakos profil fotoğrafı

Kyriakos6 ay önce

AI needs real world datasets

Manideep profil fotoğrafı

Manideep6 ay önce

this is goated startup in this batch

Jai Bhatia profil fotoğrafı

Jai Bhatia6 ay önce

Congrats boys!

Skyler Chan profil fotoğrafı

Skyler Chan6 ay önce

congrats raj!

BTCaveman. profil fotoğrafı

BTCaveman.6 ay önce

Lfg boys! Kill it ! Proud to see my Indians together like this

The Humanoid Hub profil fotoğrafı

The Humanoid Hub6 ay önce

Models are hungry!!

Mohd Tanveer profil fotoğrafı

Mohd Tanveer6 ay önce

Exciting step forward for embodied AI and robotics. Congrats to the team! 🚀

Chain Alpha profil fotoğrafı

Chain Alpha6 ay önce

Congrats!

Rushil Agarwal profil fotoğrafı

Rushil Agarwal6 ay önce

Super excited

sreekar nagulapalli profil fotoğrafı

sreekar nagulapalli6 ay önce

let’s goooo

Rithvik profil fotoğrafı

Rithvik6 ay önce

Goats

Sanjay Adhikesaven profil fotoğrafı

Sanjay Adhikesaven6 ay önce

congrats!

Abhishek Vadapalli profil fotoğrafı

Abhishek Vadapalli6 ay önce

Congrats @babugi28!!

parag parikh profil fotoğrafı

parag parikh6 ay önce

Congratulations and Goodluck ...

Chawit profil fotoğrafı

Chawit6 ay önce

looks amazing!

vishnu manoj profil fotoğrafı

vishnu manoj6 ay önce

lfgg🚀

Testimony | Product Designer profil fotoğrafı

Testimony | Product Designer6 ay önce

put enough sensors on a human doing manual labor and suddenly you have the most expensive dishwashing video ever recorded. congrats on the launch

justin wang profil fotoğrafı

justin wang6 ay önce

congrats on the launch raj!!

Shamim Hossain profil fotoğrafı

Shamim Hossain6 ay önce

Congratulations on the launch!

interstellar profil fotoğrafı

interstellar6 ay önce

Great !

Mohamed Anis profil fotoğrafı

Mohamed Anis6 ay önce

Excited to see how this enables the next generation of foundation models for physical AI.

The Daily_Ai profil fotoğrafı

The Daily_Ai6 ay önce

Exciting stuff! The integration of all those data types will really push robotics forward. Can't wait to see what comes next!

Rayan profil fotoğrafı

Rayan6 ay önce

Congrats !

Ali Taha Brown profil fotoğrafı

Ali Taha Brown6 ay önce

Congrats team

Neel Sharma profil fotoğrafı

Neel Sharma6 ay önce

BANG

Keshav Agarwal profil fotoğrafı

Keshav Agarwal6 ay önce

We have build some apps that we would soon be launching on X and would YC to have a look if they find interesting

Veer Sahasi profil fotoğrafı

Veer Sahasi6 ay önce

Awesome!!

Shrinandan Narayanan profil fotoğrafı

Shrinandan Narayanan6 ay önce

Congrats guys!

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MTS

11,676 görüntüleme • 3 ay önce

.Polymath is training world generation models to automate the creation of RL environments. Traditionally, RL environment generation has been bottlenecked by human data. Superintelligence will never be achieved by human data alone. Polymath is building the core technology to enable automated environment generation using far less human effort than traditionally required, and eventually none. This allows for more complex and realistic worlds, and higher quality, scale, and diversity of tasks. This will be essential to unlock RL scaling. The end goal is to create large-scale, long-horizon environments from a text description alone. This will enable the creation of worlds of arbitrary complexity and scale, which is foundational for training & evaluating autonomous, superintelligent AI agents. Congrats on the launch, Dylan Ma and Naren Yenuganti!

.Polymath is training world generation models to automate the creation of RL environments. Traditionally, RL environment generation has been bottlenecked by human data. Superintelligence will never be achieved by human data alone. Polymath is building the core technology to enable automated environment generation using far less human effort than traditionally required, and eventually none. This allows for more complex and realistic worlds, and higher quality, scale, and diversity of tasks. This will be essential to unlock RL scaling. The end goal is to create large-scale, long-horizon environments from a text description alone. This will enable the creation of worlds of arbitrary complexity and scale, which is foundational for training & evaluating autonomous, superintelligent AI agents. Congrats on the launch, Dylan Ma and Naren Yenuganti!

Y Combinator

44,192 görüntüleme • 6 ay önce

The first open-source unified world model for scalable robot manipulation: 5B-parameter open-source unified video-action world model that combines policy and world modeling to generate robot actions, predict future visuals, and evaluate task progress from observations, language, and state. The model is trained on 27.3K hours of heterogeneous data (17.8K real-robot teleop, 6.5K UMI demos, 3K egocentric human videos), enabling it to perform complex manipulation tasks like faucet connecting, bag packing, and toolbox storing as shown in demo videos. The approach supports test-time action refinement and points toward deployment-driven continuous improvement via fleet data. Thanks for sharing, Jianlan Luo (Jianlan Luo)! 📌 Resource links for τ0-WM: • Project page: • GitHub (code): • Hugging Face (model weights): • Paper (PDF): ——- Weekly robotics and AI insights. Subscribe free:

The first open-source unified world model for scalable robot manipulation: 5B-parameter open-source unified video-action world model that combines policy and world modeling to generate robot actions, predict future visuals, and evaluate task progress from observations, language, and state. The model is trained on 27.3K hours of heterogeneous data (17.8K real-robot teleop, 6.5K UMI demos, 3K egocentric human videos), enabling it to perform complex manipulation tasks like faucet connecting, bag packing, and toolbox storing as shown in demo videos. The approach supports test-time action refinement and points toward deployment-driven continuous improvement via fleet data. Thanks for sharing, Jianlan Luo (Jianlan Luo)! 📌 Resource links for τ0-WM: • Project page: • GitHub (code): • Hugging Face (model weights): • Paper (PDF): ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

28,276 görüntüleme • 3 ay önce

For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Today, we take a major stride toward making that dream a reality: Introducing Gemini Robotics 2 from Google DeepMind, the intelligence layer powering the next generation of truly adaptable robots. This major advance unlocks intelligent whole-body control, advanced dexterity, and even multi-robot collaboration 🤯. Ok but... how does a robot actually "think"? Real-world tasks take time and planning. To manage that complexity, our new embodied reasoning model, Gemini Robotics ER 2, acts as the robot’s high-level brain, enhancing the robot’s capabilities to: — Observe the environment — Reason about the actions needed to complete the task — Coordinate with the vision-language-action model to carry out actions — Track progress until the job is done This setup allows robots to execute complex multi-step workflows, self-correct if a step fails, and adapt to completely novel situations. Learn more about Gemini Robotics ER 2 (and our two other brand new models) here:

Google AI

783,533 görüntüleme • 1 ay önce

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,576 görüntüleme • 1 ay önce