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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 просмотров • 6 месяцев назад •via X (Twitter)

Искусство Образование Наука и технологии

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Комментарии: 34

Фото профиля Runtime

Runtime6 месяцев назад

Onwards and upwards!

Фото профиля Panda Hodler (YC Intern)

Panda Hodler (YC Intern)6 месяцев назад

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

Фото профиля WildPinesAI

WildPinesAI6 месяцев назад

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

Фото профиля Samay Maini

Samay Maini6 месяцев назад

let’s archive the world for embodied intelligence!

Фото профиля Kai

Kai6 месяцев назад

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.

Фото профиля Maahir Sachdev

Maahir Sachdev6 месяцев назад

congrats champ

Фото профиля Kyriakos

Kyriakos6 месяцев назад

AI needs real world datasets

Фото профиля Manideep

Manideep6 месяцев назад

this is goated startup in this batch

Фото профиля Jai Bhatia

Jai Bhatia6 месяцев назад

Congrats boys!

Фото профиля Skyler Chan

Skyler Chan6 месяцев назад

congrats raj!

Фото профиля BTCaveman.

BTCaveman.6 месяцев назад

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

Фото профиля The Humanoid Hub

The Humanoid Hub6 месяцев назад

Models are hungry!!

Фото профиля Mohd Tanveer

Mohd Tanveer6 месяцев назад

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

Фото профиля Chain Alpha

Chain Alpha6 месяцев назад

Congrats!

Фото профиля Rushil Agarwal

Rushil Agarwal6 месяцев назад

Super excited

Фото профиля sreekar nagulapalli

sreekar nagulapalli6 месяцев назад

let’s goooo

Фото профиля Rithvik

Rithvik6 месяцев назад

Goats

Фото профиля Sanjay Adhikesaven

Sanjay Adhikesaven6 месяцев назад

congrats!

Фото профиля Abhishek Vadapalli

Abhishek Vadapalli6 месяцев назад

Congrats @babugi28!!

Фото профиля parag parikh

parag parikh6 месяцев назад

Congratulations and Goodluck ...

Фото профиля Chawit

Chawit6 месяцев назад

looks amazing!

Фото профиля vishnu manoj

vishnu manoj6 месяцев назад

lfgg🚀

Фото профиля Testimony | Product Designer

Testimony | Product Designer6 месяцев назад

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

Фото профиля justin wang

justin wang6 месяцев назад

congrats on the launch raj!!

Фото профиля Shamim Hossain

Shamim Hossain6 месяцев назад

Congratulations on the launch!

Фото профиля interstellar

interstellar6 месяцев назад

Great !

Фото профиля Mohamed Anis

Mohamed Anis6 месяцев назад

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

Фото профиля The Daily_Ai

The Daily_Ai6 месяцев назад

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

Фото профиля Rayan

Rayan6 месяцев назад

Congrats !

Фото профиля Ali Taha Brown

Ali Taha Brown6 месяцев назад

Congrats team

Фото профиля Neel Sharma

Neel Sharma6 месяцев назад

BANG

Фото профиля Keshav Agarwal

Keshav Agarwal6 месяцев назад

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

Фото профиля Veer Sahasi

Veer Sahasi6 месяцев назад

Awesome!!

Фото профиля Shrinandan Narayanan

Shrinandan Narayanan6 месяцев назад

Congrats guys!

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60,985 просмотров • 7 месяцев назад

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Genesis AI

2,737,590 просмотров • 4 месяцев назад

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

SITUATION EXPLAINED: A new generation of AI labs are betting that world models are the path to general intelligence that can interact with the real world. We had General Intuition CEO Pim de Witte on MTS to explain what world models are, and why intuitively world models offer a clearer path towards general intelligence than LLMs. For humans, text is a technology for compressing information about the unfolding, three-dimensional world around us and communicating it to other people. When LLMs reason about the world, they do so by reconstructing the world through text. World models work by modeling the three-dimensional world at the architecture level and then allowing agents to interact with that world. Unlike an LLM, which understands space and time through text, world models perform spatial and temporal reasoning natively. General Intuition hopes to use world models to create embodied AI—machines that can reason and act in the real world. But other companies are also pushing the frontier in this space. Odyssey ML today announced a multi-agent world model, Agora-1. Their launch video shows four participants interacting with a real-time world model simulation of GoldenEye, a first-person shooter for the N64. A playable preview of the GoldenEye simulation is available on their site.

MTS

11,676 просмотров • 3 месяцев назад

.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 просмотров • 6 месяцев назад

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 просмотров • 3 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад