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introducing openego the largest opensource POV manipulation dataset 1107 hours, 120M frames, 290 tasks, 600+ environments task, language actions & 3D hand pose annotations we are collecting egocentric data at scale using AI glasses dm me if your interested in egocentric data

ahad

1,898 subscribers

44,008 görüntüleme • 1 yıl önce •via X (Twitter)

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36 Yorum

ahad profil fotoğrafı

ahad1 yıl önce

currently there is a gap in robotics world models, VLAs, VLMs, and imitation learning require data to generalize we introduce openego to address this the largest collection of open source egocentric footage with fine-grained language and hand joint annotations

ahad profil fotoğrafı

ahad1 yıl önce

paper: code: website:

ahad profil fotoğrafı

ahad1 yıl önce

also would like to thank @chris_j_paxton and @svlevine for their blog posts on robotic data that inspired this work and all the authors that open sourced their datasets that make up openego

Bercan profil fotoğrafı

Bercan1 yıl önce

Wait a second so you didnt collect any new data ?

ahad profil fotoğrafı

ahad1 yıl önce

yup, two of the dataset included was by our lab @IRVLUTD we are trying to make one place for ego manipulation data similar to open x-embodiment where anyone can contribute

Bercan profil fotoğrafı

Bercan1 yıl önce

@IRVLUTD Great initiative. I love it, but maybe in your post, make it clear it became only clear when reading the paper.

Alberto Hojel profil fotoğrafı

Alberto Hojel1 yıl önce

0:10s “right thumb pushes the power button…” is actually index finger VLM captioning ain’t there yet 😓

ahad profil fotoğrafı

ahad1 yıl önce

Yup, suprisingly a lot of this little errors you can address them with a better prompt.

maxleedev profil fotoğrafı

maxleedev1 yıl önce

this is sick

ahad profil fotoğrafı

ahad1 yıl önce

thanks! was inspired by your post 😂😂

Bruno Santos🇵🇹 profil fotoğrafı

Bruno Santos🇵🇹1 yıl önce

@YuXiang_IRVL @pablovelagomez1

Pranav Rapelli profil fotoğrafı

Pranav Rapelli1 yıl önce

great stuff

Chuck Petras profil fotoğrafı

Chuck Petras1 yıl önce

@BrianRoemmele

Diann💎🌹 profil fotoğrafı

Diann💎🌹1 yıl önce

@TheShamdoo 😈 Fade hype, but never fade MONSTA.

ζ Pedram ζ profil fotoğrafı

ζ Pedram ζ1 yıl önce

Very cool, this is the way towards Embodied AI

ahad profil fotoğrafı

ahad1 yıl önce

Yup, I believe egocentric videos is the key to scaling robot foundation models

towerofshadow profil fotoğrafı

towerofshadow1 yıl önce

this looks sick ahad

ahad profil fotoğrafı

ahad1 yıl önce

thanks!

James Han profil fotoğrafı

James Han1 yıl önce

goat

Avi profil fotoğrafı

Avi1 yıl önce

so goated

Saïd Aitmbarek profil fotoğrafı

Saïd Aitmbarek1 yıl önce

based dataset, sick work! feel free to push it to mate

Mario Sonna profil fotoğrafı

Mario Sonna1 yıl önce

Hi, we need egocentric data, how we can contact you

ahad profil fotoğrafı

ahad1 yıl önce

sure, if you want access to openego. It’s available on the website And if you want other data just reach out to me either on x or through my email at [email protected]

Boban Jankovic profil fotoğrafı

Boban Jankovic1 yıl önce

any plans to expand beyond AI glasses for data collection?

ahad profil fotoğrafı

ahad1 yıl önce

Yeah, we are collecting some data with gopro, and intel realsense cameras hooked up to a headset. Similar to some of the data in the dataset.

Chetan profil fotoğrafı

Chetan1 yıl önce

cool dataset. have you guys trained anything using it yet? any worries that occlusion causes critical hand tracking issues?

ahad profil fotoğrafı

ahad1 yıl önce

we have some experiments on trajectory prediction of hand joints. we are planning to do more experiments with it specifically for world models and IL. we have the binary visibility for when it’s occluded. for hand poses we predict we use a confidence threshold on the hand pose.

J profil fotoğrafı

J1 yıl önce

🔥🔥🔥

Lukas Die Kunst profil fotoğrafı

Lukas Die Kunst1 yıl önce

This dataset's 120M frames could transform aerodynamics validation. Have you explored applications in sports engineering or UAV simulation?

ahad profil fotoğrafı

ahad1 yıl önce

this is interesting, I haven't personally thought much on this. how can it be used?

Lukas Die Kunst profil fotoğrafı

Lukas Die Kunst1 yıl önce

Your dataset could revolutionize aerodynamic cycling gear design - UAE sports tech hubs would jump at this application.

Ran Cheng profil fotoğrafı

Ran Cheng1 yıl önce

what glasses are you using? project Aria2?

ahad profil fotoğrafı

ahad1 yıl önce

we built in house glasses

Brooke Gardner profil fotoğrafı

Brooke Gardner1 yıl önce

Wow, 600+ environments! 🤯 How do AI glasses impact data collection in such diverse settings?

Vishnu profil fotoğrafı

Vishnu1 yıl önce

Woah this is cool

Riley Ng profil fotoğrafı

Riley Ng10 ay önce

congrats man loved how you are collecting egocentric data at scale.

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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:

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Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

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467,596 görüntüleme • 1 yıl önce

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

The Humanoid Hub

11,575 görüntüleme • 7 ay önce

Excited to introduce SOLE-R1, a video-language reasoning model for zero-shot reward prediction for robot manipulation tasks! SOLE-R1 reasoning can serve as the SOLE signal for learning new tasks (completely from scratch) through online RL - i.e., robots start with random actions and learn previously unseen tasks guided only by SOLE-R1 rewards, without any demonstrations, ground-truth rewards, success indicators, or task-specific tuning. SOLE-R1 significantly outperforms strong baselines (e.g., Robometer, RoboReward, TOPReward, GPT-5, Gemini-3-Pro) in zero-shot online RL when evaluated across 40 tasks - including a real-world tabletop manipulation setting and 4 sim environments (LIBERO, ManiSkill, Meta-World, RoboSuite). We open source all models, training data, and code. Website, demos, and paper at: 🧵 (1/6)

Excited to introduce SOLE-R1, a video-language reasoning model for zero-shot reward prediction for robot manipulation tasks! SOLE-R1 reasoning can serve as the SOLE signal for learning new tasks (completely from scratch) through online RL - i.e., robots start with random actions and learn previously unseen tasks guided only by SOLE-R1 rewards, without any demonstrations, ground-truth rewards, success indicators, or task-specific tuning. SOLE-R1 significantly outperforms strong baselines (e.g., Robometer, RoboReward, TOPReward, GPT-5, Gemini-3-Pro) in zero-shot online RL when evaluated across 40 tasks - including a real-world tabletop manipulation setting and 4 sim environments (LIBERO, ManiSkill, Meta-World, RoboSuite). We open source all models, training data, and code. Website, demos, and paper at: 🧵 (1/6)

Philip Schroeder

19,740 görüntüleme • 4 ay önce

Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics

Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics

MTS

17,740 görüntüleme • 1 ay önce

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,798 görüntüleme • 3 yıl önce

🤖 Introducing InternVLA-A1 — now fully open-sourced! Many VLA models follow instructions well in static scenes… but struggle in dynamic environments (conveyor belts, rotating platforms, multi-robot setups). Why? They see the present—but can’t imagine the future. InternVLA-A1 solution: unify perception, imagination, and action in one model: ✅ Scene understanding: Image + text → task parsing ✅ Task imagination: Predict future frames → reason about dynamics ✅ Guided control: Execute actions steered by visual foresight Powered by InternData-A1 - Large-scale high-quality simulated dataset, InternVLA-A1 stays robust under complex backgrounds, lighting, and distractions. 🔥 See it in action: 1️⃣ High-speed conveyor: track, predict, and stably grasp or flip packages 2️⃣ Rotating platform: task-aware recognition & precise pick-up of diverse items 📊 Outperforms π0 and Gr00t N1.5 on general manipulation benchmarks! ✨ Model, data, and code are all open! Models: Datasets: GitHub:

🤖 Introducing InternVLA-A1 — now fully open-sourced! Many VLA models follow instructions well in static scenes… but struggle in dynamic environments (conveyor belts, rotating platforms, multi-robot setups). Why? They see the present—but can’t imagine the future. InternVLA-A1 solution: unify perception, imagination, and action in one model: ✅ Scene understanding: Image + text → task parsing ✅ Task imagination: Predict future frames → reason about dynamics ✅ Guided control: Execute actions steered by visual foresight Powered by InternData-A1 - Large-scale high-quality simulated dataset, InternVLA-A1 stays robust under complex backgrounds, lighting, and distractions. 🔥 See it in action: 1️⃣ High-speed conveyor: track, predict, and stably grasp or flip packages 2️⃣ Rotating platform: task-aware recognition & precise pick-up of diverse items 📊 Outperforms π0 and Gr00t N1.5 on general manipulation benchmarks! ✨ Model, data, and code are all open! Models: Datasets: GitHub:

ModelScope

38,111 görüntüleme • 9 ay önce