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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 次观看 • 1 年前 •via X (Twitter)

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36 条评论

ahad 的头像

ahad1 年前

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 的头像

ahad1 年前

paper: code: website:

ahad 的头像

ahad1 年前

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 的头像

Bercan1 年前

Wait a second so you didnt collect any new data ?

ahad 的头像

ahad1 年前

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 的头像

Bercan1 年前

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

Alberto Hojel 的头像

Alberto Hojel1 年前

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

ahad 的头像

ahad1 年前

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

maxleedev 的头像

maxleedev1 年前

this is sick

ahad 的头像

ahad1 年前

thanks! was inspired by your post 😂😂

Bruno Santos🇵🇹 的头像

Bruno Santos🇵🇹1 年前

@YuXiang_IRVL @pablovelagomez1

Pranav Rapelli 的头像

Pranav Rapelli1 年前

great stuff

Chuck Petras 的头像

Chuck Petras1 年前

@BrianRoemmele

Diann💎🌹 的头像

Diann💎🌹1 年前

@TheShamdoo 😈 Fade hype, but never fade MONSTA.

ζ Pedram ζ 的头像

ζ Pedram ζ1 年前

Very cool, this is the way towards Embodied AI

ahad 的头像

ahad1 年前

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

towerofshadow 的头像

towerofshadow1 年前

this looks sick ahad

ahad 的头像

ahad1 年前

thanks!

James Han 的头像

James Han1 年前

goat

Avi 的头像

Avi1 年前

so goated

Saïd Aitmbarek 的头像

Saïd Aitmbarek1 年前

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

Mario Sonna 的头像

Mario Sonna1 年前

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

ahad 的头像

ahad1 年前

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 的头像

Boban Jankovic1 年前

any plans to expand beyond AI glasses for data collection?

ahad 的头像

ahad1 年前

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 的头像

Chetan1 年前

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

ahad 的头像

ahad1 年前

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 的头像

J1 年前

🔥🔥🔥

Lukas Die Kunst 的头像

Lukas Die Kunst1 年前

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

ahad 的头像

ahad1 年前

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

Lukas Die Kunst 的头像

Lukas Die Kunst1 年前

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

Ran Cheng 的头像

Ran Cheng1 年前

what glasses are you using? project Aria2?

ahad 的头像

ahad1 年前

we built in house glasses

Brooke Gardner 的头像

Brooke Gardner1 年前

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

Vishnu 的头像

Vishnu1 年前

Woah this is cool

Riley Ng 的头像

Riley Ng10 个月前

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

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467,596 次观看 • 1 年前

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 次观看 • 7 个月前

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 次观看 • 4 个月前

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 次观看 • 1 个月前

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 次观看 • 3 年前

🤖 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 次观看 • 9 个月前