Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

A real-to-sim-to-real approach for more precise home robots. Full video:

MIT CSAIL

341,115 subscribers

50,619 görüntüleme • 2 yıl önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

6 Yorum

David Yenicelik profil fotoğrafı

David Yenicelik2 yıl önce

Whats the latent embedding? Is it interpretable in any way? Is there even one?

Zeno Mercer profil fotoğrafı

Zeno Mercer2 yıl önce

Here for it!

./.-//.//|..///.- profil fotoğrafı

./.-//.//|..///.-2 yıl önce

this is badass

Kiran Prabhu profil fotoğrafı

Kiran Prabhu2 yıl önce

Cool!!!

Vincent Valentine (CEO of UnOpen.ai) profil fotoğrafı

Vincent Valentine (CEO of UnOpen.ai)2 yıl önce

Intriguing approach! Have you explored its scalability for complex environments?

Fang Jiading profil fotoğrafı

Fang Jiading2 yıl önce

How do you assign physical properties into the reconstructed objects to make it simulate-able?

Benzer Videolar

Sim-to-real learning for humanoid robots is a full-stack problem. Today, Amazon FAR is releasing a full-stack solution: Holosoma. To accelerate research, we are open-sourcing a complete codebase covering multiple simulation backends, training, retargeting, and real-world inference.

Sim-to-real learning for humanoid robots is a full-stack problem. Today, Amazon FAR is releasing a full-stack solution: Holosoma. To accelerate research, we are open-sourcing a complete codebase covering multiple simulation backends, training, retargeting, and real-world inference.

Carlo Sferrazza

218,382 görüntüleme • 10 ay önce

Figure 02 robots are now walking with a smoother gait and increased speed, thanks to sim-to-real reinforcement learning.

Figure 02 robots are now walking with a smoother gait and increased speed, thanks to sim-to-real reinforcement learning.

The Humanoid Hub

146,868 görüntüleme • 1 yıl önce

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)

Zhenyu Jiang

44,844 görüntüleme • 1 yıl önce

The Real-to-Sim part of R2S2R transforms physical robots, sensors, environments, objects, and interactions into simulations that preserve task-relevant observations and dynamics - not only how the world looks, but how it acts when the robot interacts with it. These results set a new bar for sim-real alignment in robot manipulation.

The Real-to-Sim part of R2S2R transforms physical robots, sensors, environments, objects, and interactions into simulations that preserve task-relevant observations and dynamics - not only how the world looks, but how it acts when the robot interacts with it. These results set a new bar for sim-real alignment in robot manipulation.

Fei-Fei Li

26,186 görüntüleme • 2 ay önce

🇨🇳 China moves from robot demos to real deployment. The plan is to deploy 10,000 humanoid robots in more than 100 real-world applications by year-end.

🇨🇳 China moves from robot demos to real deployment. The plan is to deploy 10,000 humanoid robots in more than 100 real-world applications by year-end.

Jackson Hinkle 🇺🇸

50,061 görüntüleme • 3 ay önce

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

NVIDIA Robotics

10,840 görüntüleme • 5 ay önce

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

Unreal Engine

61,187 görüntüleme • 5 ay önce

Advancing dexterous manipulation through scalable visual sim-to-real transfer. We are excited to share our RSS paper, “ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation.” 🌐 Project page: 1/N 🧵

Advancing dexterous manipulation through scalable visual sim-to-real transfer. We are excited to share our RSS paper, “ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation.” 🌐 Project page: 1/N 🧵

Robotic Systems Lab

40,380 görüntüleme • 4 ay önce

ROBOT FAINTS WHEN HE SEES CHINA'S DANCING ROBOTS! (Full disclosure: I spliced a video of my hand holding a phone into a real video for comedic purposes. No AI was used)

ROBOT FAINTS WHEN HE SEES CHINA'S DANCING ROBOTS! (Full disclosure: I spliced a video of my hand holding a phone into a real video for comedic purposes. No AI was used)

Nury Vittachi

15,983 görüntüleme • 7 ay önce

The "sim-to-real" gap just got a massive 2,300m² bridge. 🤖🏗️ NEURA Robotics and TUM are launching the TUM RoboGym at Munich Airport—Europe’s largest training center for Physical AI. Starting mid-2026, a fleet of humanoid robots will begin real-world training to fuel the Neuraverse ecosystem with high-quality, sovereign data.

The "sim-to-real" gap just got a massive 2,300m² bridge. 🤖🏗️ NEURA Robotics and TUM are launching the TUM RoboGym at Munich Airport—Europe’s largest training center for Physical AI. Starting mid-2026, a fleet of humanoid robots will begin real-world training to fuel the Neuraverse ecosystem with high-quality, sovereign data.

Humanoids daily

25,560 görüntüleme • 6 ay önce

Today we give Atlas dexterous hands, with the strength to take on real work, and the simplicity for mass manufacturing. Like the body, the hands are designed with high fidelity simulation in mind, to enable sim-to-real RL. If you give dexterous hands to a robot, make them do dexterous things. More thoughts here Blog: Video:

Today we give Atlas dexterous hands, with the strength to take on real work, and the simplicity for mass manufacturing. Like the body, the hands are designed with high fidelity simulation in mind, to enable sim-to-real RL. If you give dexterous hands to a robot, make them do dexterous things. More thoughts here Blog: Video:

Alberto Rodriguez

550,651 görüntüleme • 3 gün önce

For robots to be useful, they must be able to interact with a wide variety of environments; and yet, scaling interaction data is difficult, expensive, and time consuming. Instead, much research revolves around sim-to-real manipulation — but mostly this has not been mobile manipulation. Recently, though, this has begun to change. Two recent papers from Tairan He and Haoru Xue show us how to unlock the potential of this technique, building policies which, without any real data at all, can move objects around in the world and open doors in the real world with a humanoid robot. Watch Episode #60 of RoboPapers now to learn more, hosted by Chris Paxton and Jiafei Duan. In this episode, we cover two papers:. First is VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation; and second is DoorMan: Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer.

For robots to be useful, they must be able to interact with a wide variety of environments; and yet, scaling interaction data is difficult, expensive, and time consuming. Instead, much research revolves around sim-to-real manipulation — but mostly this has not been mobile manipulation. Recently, though, this has begun to change. Two recent papers from Tairan He and Haoru Xue show us how to unlock the potential of this technique, building policies which, without any real data at all, can move objects around in the world and open doors in the real world with a humanoid robot. Watch Episode #60 of RoboPapers now to learn more, hosted by Chris Paxton and Jiafei Duan. In this episode, we cover two papers:. First is VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation; and second is DoorMan: Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer.

RoboPapers

30,767 görüntüleme • 8 ay önce

This is real. Robots at home will be mainstream in <5 years. X1, Optimus, Atlas, Figure 01 and many more

This is real. Robots at home will be mainstream in <5 years. X1, Optimus, Atlas, Figure 01 and many more

Linus ✦ Ekenstam

397,161 görüntüleme • 2 yıl önce