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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

1,648,997 subscribers

110,791 views • 6 months ago •via X (Twitter)

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34 Comments

Runtime6 months ago

Onwards and upwards!

Panda Hodler (YC Intern)6 months ago

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

WildPinesAI6 months ago

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

Samay Maini6 months ago

let’s archive the world for embodied intelligence!

Kai6 months ago

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 Sachdev6 months ago

congrats champ

Kyriakos6 months ago

AI needs real world datasets

Manideep6 months ago

this is goated startup in this batch

Jai Bhatia6 months ago

Congrats boys!

Skyler Chan6 months ago

congrats raj!

BTCaveman.6 months ago

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

The Humanoid Hub6 months ago

Models are hungry!!

Mohd Tanveer6 months ago

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

Chain Alpha6 months ago

Congrats!

Rushil Agarwal6 months ago

Super excited

sreekar nagulapalli6 months ago

let’s goooo

Rithvik6 months ago

Goats

Sanjay Adhikesaven6 months ago

congrats!

Abhishek Vadapalli6 months ago

Congrats @babugi28!!

parag parikh6 months ago

Congratulations and Goodluck ...

Chawit6 months ago

looks amazing!

vishnu manoj6 months ago

lfgg🚀

Testimony | Product Designer6 months ago

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 wang6 months ago

congrats on the launch raj!!

Shamim Hossain6 months ago

Congratulations on the launch!

interstellar6 months ago

Great !

Mohamed Anis6 months ago

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

The Daily_Ai6 months ago

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

Rayan6 months ago

Congrats !

Ali Taha Brown6 months ago

Congrats team

Neel Sharma6 months ago

BANG

Keshav Agarwal6 months ago

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 Sahasi6 months ago

Awesome!!

Shrinandan Narayanan6 months ago

Congrats guys!

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Ilir Aliu

28,276 views • 3 months ago

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

783,491 views • 1 month ago

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 views • 1 month ago