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🤖 Robotics often faces a chicken and egg problem: no web-scale robot data for training (unlike CV or NLP) b/c robots aren't deployed yet & vice-versa. Introducing VRB: Use large-scale human videos to train a general-purpose affordance model to jumpstart any robotics paradigm!

Deepak Pathak

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70,712 görüntüleme • 3 yıl önce •via X (Twitter)

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Deepak Pathak3 yıl önce

🌐 Given a new scene, our general-purpose VRB model predicts all the locations *where* a robot can manipulate objects and *how* should it move post-manipulation. 💡Qs: 1) What are affordances? 2) How to get large-scale data? 3) How does this help robots?

Deepak Pathak profil fotoğrafı

Deepak Pathak3 yıl önce

🧩 In computer vision, there is a long line of work in affordances (Gibson 1966, 1979). However, what's the best way to define them for robotics? Our solution: interaction points & post-contact trajectories, allowing for seamless integration with almost any robot learning setup.

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Deepak Pathak3 yıl önce

🔧🔧 How do we extract affordances from humans? We estimate hand poses & hand-object interaction points to extract contact pts + interaction direction (thanks to @DandanShan_, David Fouhey's 100DoH), and map them back to frames where humans arent present to avoid embodiment gap.

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Deepak Pathak3 yıl önce

How to use this model for different robot learning paradigms? We show 4 different paradigms. 1. Offline Data Collection 📊 Instead of using teleoperation or scripted policies, VRB can allow for an automatic collection of good quality interaction-rich data for policy learning.

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Deepak Pathak3 yıl önce

2. Bootstrapping Exploration 🔍 Robots can utilize the predicted affordances from VRB to explore their environment more intelligently, leading to the discovery of novel and effective ways to manipulate objects.

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Deepak Pathak3 yıl önce

3. Goal-Conditioned Learning 🎯 Can we use our affordance model to iteratively improve performance? We train goal-oriented policies using actions sampled from VRB. We prune this action distribution using the distance to the goal during the training process (using WHIRL RSS'22).

Deepak Pathak profil fotoğrafı

Deepak Pathak3 yıl önce

4. Action Space Reparameterization 🔄 We can also treat our affordances as an action space itself. It helps accelerate online RL eliminating the need for task-specific primitives. This enables DQN to learn manipulation tasks in under 30 mins!

Deepak Pathak profil fotoğrafı

Deepak Pathak3 yıl önce

Using internet-scale videos is a promising approach to tackling the data problem in robotics and we hope VRB is a strong step toward that! Led by @shikharbahl, @mendonca_rl, @lchen915, @unnatjain2010 CVPR 2023 Paper: Website: 9/9

Murtaza Dalal profil fotoğrafı

Murtaza Dalal3 yıl önce

Awesome work by my colleagues @shikharbahl, @mendonca_rl, @lchen915! Extracting affordances from human video enables robots to efficiently solve a wide array of manipulation tasks in the real world 🤖. Excited to see where this goes next!

Siyuan HUANG profil fotoğrafı

Siyuan HUANG3 yıl önce

Great ideas and a very interesting presentation. :-) I have checked your website but find the dataset is coming soon, would that be available soon?

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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

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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.

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Robotics is entering the mainstream! 👀 Let's accept this fact. Consumptionism – stonks. Possibilities (HW + SW) – stonks. Demographics - not stonks. Modern robots are often designed to interact with our legacy world, whether it's a household, a factory, or a shipyard. That means they often need to work with existing machines, tools, and spaces. In short: retrofitting. Improving old machines with new technologies without major interference in their operation. Modern way of integrating things. You don't have to change the entire environment to deploy a robot. The robot will adapt, just as human would when freshly hired. The promise is to have an army of robots to help with the world designed for us. I also believe that general purpose robots, are not a direct competition for single purpose machines (like a highly efficient specialized packaging machine, for example). It's like adding a robot to this carriage. Specialized machine could be a car ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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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

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Collecting robot data at scale is key to deploying working manipulation policies, and the team from Tutor Intelligence Tutor is here to tell us about how to accomplish it. Their new announcement: a massive, 100-robot “data factory,” with a behind-the-scenes look at how to build a teleoperation platform and how to make robots and policies that are useful for their customers. Tutor Intelligence is a full-stack robotics company: they build robot arms, they sell robot arms, they write the software and they train neural networks. Josh Gruenstein, Jesse Michel, Shiraz, and Joe McCalmon, and Joe McCalmon join us to tell us more about how they scale both teleop data and human interventions from their teleoperators in order to train the policies they need. Watch Episode #85 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

36,041 görüntüleme • 3 ay önce

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,835 görüntüleme • 8 ay önce