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.

Introducing a way to convert synthesized robot videos to robot execution without using any action labels! We also release a codebase (with pretrained models) for text-to-video generation. Train your own models for robot control in only 1 day with 4 GPUs!

Yilun Du

9,488 subscribers

20,949 views • 2 years ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

1 Comments

Yilun Du2 years ago

Work led by @PoChen23602 with @maojiayuan , @shaohua0116 , and Josh Tenenbaum.

Related Videos

Vision-language models perform diverse tasks via in-context learning. Time for robots to do the same! Introducing In-Context Robot Transformer (ICRT): a robot policy that learns new tasks by prompting with robot trajectories, without any fine-tuning. [1/N]

Vision-language models perform diverse tasks via in-context learning. Time for robots to do the same! Introducing In-Context Robot Transformer (ICRT): a robot policy that learns new tasks by prompting with robot trajectories, without any fine-tuning. [1/N]

Max Fu

40,435 views • 1 year ago

Proud to share what I’ve been working on with my colleagues at Rhoda AI: Direct Video-Action Models (DVA). TL;DR: - We pre-train causal video models from scratch to control robots - They handle complex production tasks for hours without intervention - Only use ~10 hours of robot data How? 🧵👇

Proud to share what I’ve been working on with my colleagues at Rhoda AI: Direct Video-Action Models (DVA). TL;DR: - We pre-train causal video models from scratch to control robots - They handle complex production tasks for hours without intervention - Only use ~10 hours of robot data How? 🧵👇

Tongzhou Mu 🤖🦾🦿

19,102 views • 3 months ago

Physical Intelligence's π₀, a general-purpose robot foundation model that combines Internet-scale vision-language pretraining with robot interaction data to execute tasks. They aim "to develop foundation models that can control any robot to perform any task" Autonomous demos:

Physical Intelligence's π₀, a general-purpose robot foundation model that combines Internet-scale vision-language pretraining with robot interaction data to execute tasks. They aim "to develop foundation models that can control any robot to perform any task" Autonomous demos:

The Humanoid Hub

196,055 views • 1 year ago

Vision-language models can control robots, but what if the prompt is too complex for the robot to follow directly? We developed a way to get robots to “think through” complex instructions, feedback, and interjections. We call it the Hierarchical Interactive Robot (Hi Robot).

Vision-language models can control robots, but what if the prompt is too complex for the robot to follow directly? We developed a way to get robots to “think through” complex instructions, feedback, and interjections. We call it the Hierarchical Interactive Robot (Hi Robot).

Physical Intelligence

116,845 views • 1 year ago

Introducing an approach to directly ground video generation models to policy execution without needing any action labels! Our approach uses a generic goal-conditioned exploration procedure to learn a policy that works across robots / embodiments!

Introducing an approach to directly ground video generation models to policy execution without needing any action labels! Our approach uses a generic goal-conditioned exploration procedure to learn a policy that works across robots / embodiments!

Yilun Du

21,392 views • 1 year ago

Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉

Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models as-is. Introducing: Real2Render2Real 👉

Max Fu

69,224 views • 1 year ago

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company 1X has a solution: world models. 1X Director of Evaluations Daniel Ho joins us on RoboPapers to talk about: - why world models are the future for scaling robot learning - how to use world models for robot control - what world models unlock for evaluating robot model performance - how we can hill-climb from here to general purpose robots Watch Episode #61 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton, now!

RoboPapers

27,567 views • 4 months ago

Those cool robot dances are getting way easier. AgiBot's new LinkCraft platform lets you train humanoids with zero code, using AI to convert human video performances directly into robot motion. Big accessibility win!

Those cool robot dances are getting way easier. AgiBot's new LinkCraft platform lets you train humanoids with zero code, using AI to convert human video performances directly into robot motion. Big accessibility win!

RoboHub🤖

14,024 views • 8 months ago

Tired of collecting demonstrations all day to train your robot? Introducing MimicGen, an autonomous data generation system for robotics. Using just 200 human demos we generated a large multi-task dataset of 50K demos! #CoRL2023 #NVIDIAResearch 👇 🧵 1/

Tired of collecting demonstrations all day to train your robot? Introducing MimicGen, an autonomous data generation system for robotics. Using just 200 human demos we generated a large multi-task dataset of 50K demos! #CoRL2023 #NVIDIAResearch 👇 🧵 1/

Ajay Mandlekar

93,632 views • 2 years ago

Super excited for the release of Robot Utility Models (RUMs)! RUMs is a simple method to build zero-shot robot policies that can solve useful tasks in completely new homes without any additional training often at 90%+ success rate. 🧵👇

Super excited for the release of Robot Utility Models (RUMs)! RUMs is a simple method to build zero-shot robot policies that can solve useful tasks in completely new homes without any additional training often at 90%+ success rate. 🧵👇

Lerrel Pinto

56,591 views • 1 year ago

What representation enables open-world robot manipulation from generated videos? Introducing Dream2Flow, our recent work that bridges video generation and robot control with 3D object flow. Stanford University #ICRA2026 1/N

What representation enables open-world robot manipulation from generated videos? Introducing Dream2Flow, our recent work that bridges video generation and robot control with 3D object flow. Stanford University #ICRA2026 1/N

Wenlong Huang

105,597 views • 3 months ago

Tired of endlessly teleoperating your robot in order to train it? Introducing SkillMimicGen, a data generation system that automatically scales robot imitation learning by synthesizing demos through integrating motion planning and demo adaptation. 1/

Tired of endlessly teleoperating your robot in order to train it? Introducing SkillMimicGen, a data generation system that automatically scales robot imitation learning by synthesizing demos through integrating motion planning and demo adaptation. 1/

Ajay Mandlekar

35,575 views • 1 year ago

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,804 views • 4 months ago

How to harness foundation models for *generalization in the wild* in robot manipulation? Introducing VoxPoser: use LLM+VLM to label affordances and constraints directly in 3D perceptual space for zero-shot robot manipulation in the real world! 🌐 🧵👇

How to harness foundation models for generalization in the wild in robot manipulation? Introducing VoxPoser: use LLM+VLM to label affordances and constraints directly in 3D perceptual space for zero-shot robot manipulation in the real world! 🌐 🧵👇

Wenlong Huang

293,876 views • 3 years ago

Robot foundation models are limited by costly real data, while simulation data is plentiful but visually mismatched to reality. We present Point Bridge, a method that enables zero-shot sim-to-real transfer for robot learning with minimal visual alignment.

Robot foundation models are limited by costly real data, while simulation data is plentiful but visually mismatched to reality. We present Point Bridge, a method that enables zero-shot sim-to-real transfer for robot learning with minimal visual alignment.

Siddhant Haldar

19,818 views • 4 months ago

Excited to share RoCo: Dialectic Multi-Robot Collaboration with Large Language Models. We propose a novel approach to multi-robot collaboration that leverages LLMs for both high-level communication and low-level path planning. w/ Shreeya Jain, Shuran Song

Excited to share RoCo: Dialectic Multi-Robot Collaboration with Large Language Models. We propose a novel approach to multi-robot collaboration that leverages LLMs for both high-level communication and low-level path planning. w/ Shreeya Jain, Shuran Song

Mandi Zhao

88,704 views • 2 years ago

Just told a robot what to do in plain English. Launching today: Control robots with text prompts. No coding. No setup. No expensive GPUs. No ML PhD needed. Launch app, input prompt, BOOM—AI controls your robot. Need more? Record examples, we fine-tune it. Try now! Link below

Just told a robot what to do in plain English. Launching today: Control robots with text prompts. No coding. No setup. No expensive GPUs. No ML PhD needed. Launch app, input prompt, BOOM—AI controls your robot. Need more? Record examples, we fine-tune it. Try now! Link below

Pierre-Louis Biojout (PLB)

43,759 views • 1 year ago

Developing general-purpose robot models is costly due to the need for extensive real-world data. 🤖 #NVIDIAIsaac GR00T addresses these challenges by providing #humanoidrobot developers with robot foundation models & more. #CES2025 📹

Developing general-purpose robot models is costly due to the need for extensive real-world data. 🤖 #NVIDIAIsaac GR00T addresses these challenges by providing #humanoidrobot developers with robot foundation models & more. #CES2025 📹

NVIDIA Robotics

19,055 views • 1 year ago

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

Chuang Gan

43,265 views • 1 year ago

Mecka is excited to power EgoVerse: a growing ecosystem for robot learning from egocentric human data Proven across 4 leading research labs, EgoVerse data consistently boosts robot performance We release the tools for anyone to collect data, inference/train, and contribute! 🧵

Mecka is excited to power EgoVerse: a growing ecosystem for robot learning from egocentric human data Proven across 4 leading research labs, EgoVerse data consistently boosts robot performance We release the tools for anyone to collect data, inference/train, and contribute! 🧵

Josh

102,105 views • 3 months ago