Загрузка видео...

Не удалось загрузить видео

На главную

Check out our #ICRA2025 paper ! Robust, precise, and terrain-aware—our RL controller significantly improves over baselines for whole-body 6-DoF tracking.💥 Project website: Work led by: Tifanny Portela, Andrei Cramariuc, Mayank Mittal and Marco Hutter

leggedrobotics's profile picture

Robotic Systems Lab

26,168 subscribers

16,139 просмотров • 1 год назад •via X (Twitter)

Наука и технологииОбразование#ICRA2025
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 2

Фото профиля qTask
qTask1 год назад

Impressive work in advancing whole-body 6-DoF tracking with your terrain-aware RL controller! The noteworthy improvements over baselines showcase the strength of merging robustness and precision in robotic control. Excited to see how this research enhances real-world robot mobility and autonomy at #ICRA2025. Kudos to TifannyPortela, Andrei Cramariuc, Mayank Mittal, and Marco Hutter for leading this innovative effort! 🚀🤖

Фото профиля SaoAI
SaoAI1 год назад

@TifannyPortela Fab work, fam! 😍✨ #Slay 💪🎉

Похожие видео

Check out our #ICRA2025 paper where we train a home robot to walk quietly! Project site: Video: Authors: Ryo Watanabe, Takahiro Miki, Fan Shi, Yuki Kadokawa, Filip Bjelonic, Kento Kawaharazuka, Andrei Cramariuc and Marco Hutter
0:37

Check out our #ICRA2025 paper where we train a home robot to walk quietly! Project site: Video: Authors: Ryo Watanabe, Takahiro Miki, Fan Shi, Yuki Kadokawa, Filip Bjelonic, Kento Kawaharazuka, Andrei Cramariuc and Marco Hutter

Robotic Systems Lab

32,614 просмотров • 1 год назад

How can lightweight drones without depth cameras navigate using monocular images? Check out our paper at ISER 2023! MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction arXiv: website: Work led by Nate Simon
0:20

How can lightweight drones without depth cameras navigate using monocular images? Check out our paper at ISER 2023! MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction arXiv: website: Work led by Nate Simon

Anirudha Majumdar

27,642 просмотров • 2 лет назад

Mobile-TeleVision follows our previous idea on upper-body and lower-body separation for control: 1⃣We train RL for lower-body locomotion and standing; 2⃣We use IK and retargeting directly for upper-body for high-precision and smooth manipulation. We avoid using RL for upper-body motion tracking. This is because fundamentally it is hard to avoid errors with RL tracking, and sim2real gap brings another gap. At this point, we still find using IK is more practical and precise.
1:15

Mobile-TeleVision follows our previous idea on upper-body and lower-body separation for control: 1⃣We train RL for lower-body locomotion and standing; 2⃣We use IK and retargeting directly for upper-body for high-precision and smooth manipulation. We avoid using RL for upper-body motion tracking. This is because fundamentally it is hard to avoid errors with RL tracking, and sim2real gap brings another gap. At this point, we still find using IK is more practical and precise.

Xiaolong Wang

32,662 просмотров • 1 год назад

Our work WorldScribe led by Ruei-Che Chang on generating context-aware live visual descriptions has received 🏆✨ Best Paper Award ✨ at #UIST2024!! Check out our new video below, and details in the thread! Here is what NotebookLM thought about it:
4:43

Our work WorldScribe led by Ruei-Che Chang on generating context-aware live visual descriptions has received 🏆✨ Best Paper Award ✨ at #UIST2024!! Check out our new video below, and details in the thread! Here is what NotebookLM thought about it:

Anhong Guo

13,266 просмотров • 1 год назад

Check out our #ICRA2025 paper where we train a robotic puppy to dance expressively! Our method, Deep Fourier Mimic, a generalized version of DeepMimic, enables automatic parameterization of reference data. Project site: Video: 🧵
1:11

Check out our #ICRA2025 paper where we train a robotic puppy to dance expressively! Our method, Deep Fourier Mimic, a generalized version of DeepMimic, enables automatic parameterization of reference data. Project site: Video: 🧵

Robotic Systems Lab

60,728 просмотров • 1 год назад

Check out our fun work on robust view synthesis! 🤩 Using a casual video as input, our method jointly 1⃣estimates accurate camera poses and 2⃣reconstructs multiple local radiance fields for a large scene. Project🕸️:
1:41

Check out our fun work on robust view synthesis! 🤩 Using a casual video as input, our method jointly 1⃣estimates accurate camera poses and 2⃣reconstructs multiple local radiance fields for a large scene. Project🕸️:

Jia-Bin Huang

35,526 просмотров • 3 лет назад

We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.
2:15

We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.

Physical Intelligence

430,406 просмотров • 3 месяцев назад

🎉 I woke up to amazing news! Huge congrats to Ruihan Yang for winning the Best Paper Award at IROS 2024 #IROS2024! 🏆 Mentoring him has been a joy, and I’m thrilled our work is getting the recognition from the community! 🌟 Check out our paper and website: #IROS2024
0:30

🎉 I woke up to amazing news! Huge congrats to Ruihan Yang for winning the Best Paper Award at IROS 2024 #IROS2024! 🏆 Mentoring him has been a joy, and I’m thrilled our work is getting the recognition from the community! 🌟 Check out our paper and website: #IROS2024

Kiana Ehsani

18,969 просмотров • 1 год назад

RL in the real world presents some big challenges, but also some really big opportunities. In our new work, HIL-SERL, Charles Xu, Jeffrey Wu, Jianlan Luo show that real-world RL can learn a huge range of precise and robust tasks, and perform them much faster than imitation.
0:37

RL in the real world presents some big challenges, but also some really big opportunities. In our new work, HIL-SERL, Charles Xu, Jeffrey Wu, Jianlan Luo show that real-world RL can learn a huge range of precise and robust tasks, and perform them much faster than imitation.

Sergey Levine

36,489 просмотров • 1 год назад

Design development for Out, a large scale audiovisual instalation. Check out the full project on our website.
0:59

Design development for Out, a large scale audiovisual instalation. Check out the full project on our website.

XK Studio

18,370 просмотров • 2 лет назад

Training a Whole-Body Control Foundation Model -- new work from my team at Agility Robotics A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->
1:56

Training a Whole-Body Control Foundation Model -- new work from my team at Agility Robotics A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->

Chris Paxton

45,068 просмотров • 9 месяцев назад

Capturing out-of-focus photos is sooo frustrating! 😩 Check out our work on defocus control with dual-camera 📸 #CVPR2023 Our method can refocus and adjust the depth of field AFTER you capture the photograph! 🤩 Paper: Project:
1:44

Capturing out-of-focus photos is sooo frustrating! 😩 Check out our work on defocus control with dual-camera 📸 #CVPR2023 Our method can refocus and adjust the depth of field AFTER you capture the photograph! 🤩 Paper: Project:

Jia-Bin Huang

41,330 просмотров • 3 лет назад

📢 Excited to share our latest work Conference on Robot Learning: Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility! #CoRL2025 🐶Now ANYmal learns to walk (hop) from real dog motions! 📄Paper: 🌐Project Website:
1:11

📢 Excited to share our latest work Conference on Robot Learning: Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility! #CoRL2025 🐶Now ANYmal learns to walk (hop) from real dog motions! 📄Paper: 🌐Project Website:

Robotic Systems Lab

10,212 просмотров • 9 месяцев назад

We're extremely excited to announce our $15.6M Series A led by a16z! We're building the AI communications platform for logistics. Go check out our new website and book a demo: You might be surprised who calls you back ;)
2:43

We're extremely excited to announce our $15.6M Series A led by a16z! We're building the AI communications platform for logistics. Go check out our new website and book a demo: You might be surprised who calls you back ;)

HappyRobot

97,026 просмотров • 1 год назад

Paper Plates, our Paper Mario-inspired cookbook is finally live! Check it out. Tons of love and paper craft was poured into this silly project. We even have a little jingle.
0:57

Paper Plates, our Paper Mario-inspired cookbook is finally live! Check it out. Tons of love and paper craft was poured into this silly project. We even have a little jingle.

Legend’s Cookbook

48,441 просмотров • 8 месяцев назад

Lockdown 3.1 improves our 3D planar tracking tools, and there's more to come soon!
0:59

Lockdown 3.1 improves our 3D planar tracking tools, and there's more to come soon!

Chris Vranos

55,145 просмотров • 1 год назад

In our #ICML2023 paper (led by Ghada Sokar and with Rishabh Agarwal and utku ) we highlight the prevalence of dormant neurons in deep RL, and propose a simple method to mitigate it. Quick video summary here, and more details in thread!
1:07

In our #ICML2023 paper (led by Ghada Sokar and with Rishabh Agarwal and utku ) we highlight the prevalence of dormant neurons in deep RL, and propose a simple method to mitigate it. Quick video summary here, and more details in thread!

Pablo Samuel Castro

40,597 просмотров • 3 лет назад

SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware check out this SAM2 vs SAMURAI comparison! - paper: - code: - license: Apache-2.0
0:25

SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware check out this SAM2 vs SAMURAI comparison! - paper: - code: - license: Apache-2.0

SkalskiP

124,358 просмотров • 1 год назад

Come check out Adele on our website, along with many other masks and products for masking and prosthetic makeup!
0:46

Come check out Adele on our website, along with many other masks and products for masking and prosthetic makeup!

Crea Fx

15,163 просмотров • 4 месяцев назад