Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) #CVPR2026, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗

Chenhao Li

7,482 subscribers

22,062 views • 4 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

The most frustrating part of imitation learning is collecting huge amounts of teleop data. But why teleop robots when robots can learn by watching us? Introducing Point Policy, a novel framework that enables robots to learn from human videos without any teleop, sim2real, or RL.

The most frustrating part of imitation learning is collecting huge amounts of teleop data. But why teleop robots when robots can learn by watching us? Introducing Point Policy, a novel framework that enables robots to learn from human videos without any teleop, sim2real, or RL.

Siddhant Haldar

69,056 views • 1 year ago

YouTube is a LARGE dataset of demonstration videos to train Generalist robot agents, but lacks action data. How can we learn DEXTEROUS skills from them? In #CoRL2024, we explore the problem of learning a Generalist Piano Playing agent from YouTube videos.

YouTube is a LARGE dataset of demonstration videos to train Generalist robot agents, but lacks action data. How can we learn DEXTEROUS skills from them? In #CoRL2024, we explore the problem of learning a Generalist Piano Playing agent from YouTube videos.

Julen Urain

42,451 views • 2 years ago

Human Data. Robot Data. Virtual Data. BrainCo Dexterous Data Matrix connects diverse data sources to establish a closed-loop data ecosystem, enabling robots to learn from real-world experiences and achieve more natural interactions. #BrainCo #EmbodiedAI #FutureRobotics

Human Data. Robot Data. Virtual Data. BrainCo Dexterous Data Matrix connects diverse data sources to establish a closed-loop data ecosystem, enabling robots to learn from real-world experiences and achieve more natural interactions. #BrainCo #EmbodiedAI #FutureRobotics

Tulsi Soni

67,586 views • 1 month ago

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to *use all of this non-optimal data to robustify imitation learning* with minimal requirements beyond BC. Key idea: use non-expert data to learn how to *recover* back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of *all* available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Imitation learning is great, but needs us to have (near) optimal data. We throw away most other data (failures, evaluation data, suboptimal data, undirected play data), even though this data can be really useful and way cheaper! In our new work - RISE, we show a simple way to use all of this non-optimal data to robustify imitation learning with minimal requirements beyond BC. Key idea: use non-expert data to learn how to recover back to expert data with a minimal frills offline RL that works under sparse data coverage. Allows usage of all available data, not just expert data - never throw your data away! Paper: Website: A 🧵(1/10)

Abhishek Gupta

20,644 views • 11 months ago

Archetype AI published a new paper demonstrating how an AI model can encode and predict physical behaviours. Newton, the startup's ‘Large Behavior Model’, can learn complex physics principles directly from raw sensor data, without any human guidance.

Archetype AI published a new paper demonstrating how an AI model can encode and predict physical behaviours. Newton, the startup's ‘Large Behavior Model’, can learn complex physics principles directly from raw sensor data, without any human guidance.

Brett Adcock

25,987 views • 1 year ago

We discovered an emergent property of VLAs like π0/π0.5/π0.6: as we scale up pre-training, the model learns to align human videos and robot data! This gives us a simple way to leverage human videos. Once π0.5 knows how to control robots, it can naturally learn from human video.

We discovered an emergent property of VLAs like π0/π0.5/π0.6: as we scale up pre-training, the model learns to align human videos and robot data! This gives us a simple way to leverage human videos. Once π0.5 knows how to control robots, it can naturally learn from human video.

Physical Intelligence

1,188,037 views • 9 months ago

Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

Mahi Shafiullah 🏠🤖

96,947 views • 3 months ago

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

Zhi (Leo) Wang

127,738 views • 4 months ago

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Abhishek Gupta

11,994 views • 1 year ago

Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video

Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video

Figure

46,041 views • 1 year ago

“we’ll have to have some sort of Universal Basic Income (UBI)” Elon Musk But who is going to pay? We believe our data! HUDI = Human Data Income We are building a Universal Basic Income based on our data. Our data is our asset, the new oil, but we have no transparency nor profit from it. Help us build the Human Data Income: we made great progress, but it’s just the beginning!

“we’ll have to have some sort of Universal Basic Income (UBI)” Elon Musk But who is going to pay? We believe our data! HUDI = Human Data Income We are building a Universal Basic Income based on our data. Our data is our asset, the new oil, but we have no transparency nor profit from it. Help us build the Human Data Income: we made great progress, but it’s just the beginning!

HUDI

11,400,526 views • 2 years ago

Presenting DemoDiffusion: An extremely simple approach enabling a pre-trained 'generalist' diffusion policy to follow a human-demonstration for a novel task during inference One-shot human imitation *without* requiring any paired human-robot data or online RL 🙂 1/n

Presenting DemoDiffusion: An extremely simple approach enabling a pre-trained 'generalist' diffusion policy to follow a human-demonstration for a novel task during inference One-shot human imitation without requiring any paired human-robot data or online RL 🙂 1/n

Homanga Bharadhwaj

33,088 views • 1 year ago

🤖WE HUMANS ARE TRAINING OUR AI REPLACEMENTS Humanoid robots are learning by watching humans work. Workers wear devices that capture egocentric data, showing robots exactly how tasks look from a human perspective. We are not just building robots. We are handing them our job's playbook.

🤖WE HUMANS ARE TRAINING OUR AI REPLACEMENTS Humanoid robots are learning by watching humans work. Workers wear devices that capture egocentric data, showing robots exactly how tasks look from a human perspective. We are not just building robots. We are handing them our job's playbook.

Coin Bureau

20,182 views • 4 months ago

Can we synthesize 3D human-scene interactions without learning from any 3D data? Yes! Check out Lei Li's GenZI, a novel zero-shot approach to generating 3D interactions by distilling priors from large vision-language models.

Can we synthesize 3D human-scene interactions without learning from any 3D data? Yes! Check out Lei Li's GenZI, a novel zero-shot approach to generating 3D interactions by distilling priors from large vision-language models.

Angela Dai

107,013 views • 2 years ago

Heard some people like wheels？😁 Humanoid robots are the ideal form of general-purpose robots (perfect for general AI and human-derived data). They can work without wheels — but they can also have wheels if they want. Whatever works.

Heard some people like wheels？😁 Humanoid robots are the ideal form of general-purpose robots (perfect for general AI and human-derived data). They can work without wheels — but they can also have wheels if they want. Whatever works.

Unitree

8,168,288 views • 5 months ago

Imagine robots learning new skills—without any robot data. Today, we're excited to release EgoZero: our first steps in training robot policies that operate in unseen environments, solely from data collected through humans wearing Aria smart glasses. 🧵👇

Imagine robots learning new skills—without any robot data. Today, we're excited to release EgoZero: our first steps in training robot policies that operate in unseen environments, solely from data collected through humans wearing Aria smart glasses. 🧵👇

Lerrel Pinto

42,673 views • 1 year ago

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi Patel and Kled AI as they build a massive data marketplace. If you are in need of a great data partner, reach out! More from us here (we were not investors when we filmed):

Nichole Wischoff

37,148 views • 6 months ago

Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌

Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌

Toru

61,664 views • 2 years ago

Introducing OM-1, our first robot foundation model, zero-shot generalizing to any robot: table-top arms, industrial arms and humanoids. - learned directly from human manipulation data - no teleop/robot data - close to human-level dexterity and efficiency - multi-robot collab

Introducing OM-1, our first robot foundation model, zero-shot generalizing to any robot: table-top arms, industrial arms and humanoids. - learned directly from human manipulation data - no teleop/robot data - close to human-level dexterity and efficiency - multi-robot collab

Reward AI

1,763,921 views • 24 days ago

Train the AI behind real-world robots We’re hiring Data Labelers to annotate images and videos to train Optimus & autonomous systems. You’ll work with real production data & directly impact how these systems learn and operate in the real world Come join!

Train the AI behind real-world robots We’re hiring Data Labelers to annotate images and videos to train Optimus & autonomous systems. You’ll work with real production data & directly impact how these systems learn and operate in the real world Come join!

Tesla Recruiting

666,486 views • 5 months ago