Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Introducing Universal Manipulation Exoskeleton (UME) A low-cost exoskeleton with real-time haptic torque feedback for learning autonomous policies that perform highly force-mediated, tightly space-constrained, visually occluded, whole-body, and long-horizon mobile manipulation tasks. Using UME, the teleoperator can unsheathe a heavy metal sword completely blindfolded. 🧵1/N

litian_liang's profile picture

Litian Liang

1,046 subscribers

592,917 görüntüleme • 16 gün önce •via X (Twitter)

EğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Learning bimanual, contact-rich robot manipulation policies that generalize over diverse objects has long been a challenge. Excited to share our work: Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation! 🧵1/n
0:15

Learning bimanual, contact-rich robot manipulation policies that generalize over diverse objects has long been a challenge. Excited to share our work: Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation! 🧵1/n

Xuanlin Li (Simon)

20,877 görüntüleme • 1 yıl önce

Using the classic trick of scrap metal, teleportation, and time manipulation to create a completely safe elevator... 😏
0:18

Using the classic trick of scrap metal, teleportation, and time manipulation to create a completely safe elevator... 😏

Juhana Myllys

84,885 görüntüleme • 1 yıl önce

How can non-experts quickly teach robots a variety of tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a **single hand demo**, HAND learns manipulation tasks in under **4 minutes**! 🧵
0:16

How can non-experts quickly teach robots a variety of tasks? Introducing HAND ✋, a simple, time-efficient method of training robots! Using just a **single hand demo**, HAND learns manipulation tasks in under **4 minutes**! 🧵

Jesse Zhang

31,995 görüntüleme • 1 yıl önce

Seeing ≠ feeling in robotics🤖. Collecting data without force feedback is not only frustrating but also doesn’t work for contact-rich tasks. Introducing FACTR: a low-cost teleop that lets users feel what the robot feels + a simple algo to integrate force into autonomous policies. Video doesn't do justice, but feels magical in person!🚀 Also removes the need for gravity compensation strings.
0:19

Seeing ≠ feeling in robotics🤖. Collecting data without force feedback is not only frustrating but also doesn’t work for contact-rich tasks. Introducing FACTR: a low-cost teleop that lets users feel what the robot feels + a simple algo to integrate force into autonomous policies. Video doesn't do justice, but feels magical in person!🚀 Also removes the need for gravity compensation strings.

Deepak Pathak

33,573 görüntüleme • 1 yıl önce

Track2Act: Our latest on training goal-conditioned policies for diverse manipulation in the real-world. We train a model for embodiment-agnostic point track prediction from web videos combined with embodiment-specific residual policy learning 1/n
0:14

Track2Act: Our latest on training goal-conditioned policies for diverse manipulation in the real-world. We train a model for embodiment-agnostic point track prediction from web videos combined with embodiment-specific residual policy learning 1/n

Homanga Bharadhwaj

34,971 görüntüleme • 2 yıl önce

Innovative Autonomous Exoskeleton Helping People with Disabilities
0:18

Innovative Autonomous Exoskeleton Helping People with Disabilities

Science girl

595,099 görüntüleme • 6 ay önce

exoskeleton suit controlling a robot in real-time
0:11

exoskeleton suit controlling a robot in real-time

Science girl

94,998 görüntüleme • 1 yıl önce

Humanoids 🤖 will do anything humans can do. But are state-of-the-art algorithms up to the challenge? Introducing HumanoidBench, the first-of-its-kind simulated humanoid benchmark with 27 distinct whole-body tasks requiring intricate long-horizon planning and coordination. 🧵👇
0:19

Humanoids 🤖 will do anything humans can do. But are state-of-the-art algorithms up to the challenge? Introducing HumanoidBench, the first-of-its-kind simulated humanoid benchmark with 27 distinct whole-body tasks requiring intricate long-horizon planning and coordination. 🧵👇

Carlo Sferrazza

117,138 görüntüleme • 2 yıl önce

Introducing 2️⃣ new AI systems for robotics: 🤖 ALOHA Unleashed to perform two-armed manipulation tasks 🦾 DemoStart to control a multi-fingered robotic hand They learned to tackle a range of actions requiring dexterity. Here's how. 🧵
0:09

Introducing 2️⃣ new AI systems for robotics: 🤖 ALOHA Unleashed to perform two-armed manipulation tasks 🦾 DemoStart to control a multi-fingered robotic hand They learned to tackle a range of actions requiring dexterity. Here's how. 🧵

Google DeepMind

297,118 görüntüleme • 1 yıl önce

GR-RL Going Dexterous and Precise for Long-Horizon Robotic Manipulation
0:16

GR-RL Going Dexterous and Precise for Long-Horizon Robotic Manipulation

AK

17,630 görüntüleme • 7 ay önce

ManiFeel is a benchmark for contact-rich manipulation tasks. Force- and tactile-based tasks with occlusions and partially-observable state are going to be crucial for true general purpose robotics. Website:
0:20

ManiFeel is a benchmark for contact-rich manipulation tasks. Force- and tactile-based tasks with occlusions and partially-observable state are going to be crucial for true general purpose robotics. Website:

Chris Paxton

12,063 görüntüleme • 4 ay önce

✨ Any static 3D assets ➡️ 4D dynamic worlds. Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs. It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖! Project page: Dive deeper in a 🧵: 1/n
0:12

✨ Any static 3D assets ➡️ 4D dynamic worlds. Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs. It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖! Project page: Dive deeper in a 🧵: 1/n

Chen Geng

43,033 görüntüleme • 5 ay önce

A behind-the-scenes video on how teleoperation is done. Whole-body manipulation with only a VisionPro.
0:12

A behind-the-scenes video on how teleoperation is done. Whole-body manipulation with only a VisionPro.

Xiaolong Wang

40,309 görüntüleme • 1 yıl önce

Long-horizon visual goals remain surprisingly hard for robot manipulation. We introduce Act2Goal, a goal-conditioned policy that uses a visual world model to reason about progress toward a goal, and practice it autonomously in the real world.
0:20

Long-horizon visual goals remain surprisingly hard for robot manipulation. We introduce Act2Goal, a goal-conditioned policy that uses a visual world model to reason about progress toward a goal, and practice it autonomously in the real world.

Jianlan Luo

95,100 görüntüleme • 6 ay önce

You can rent a robotic exoskeleton to hike a mountain for just $8.50.
0:10

You can rent a robotic exoskeleton to hike a mountain for just $8.50.

Massimo

117,609 görüntüleme • 1 yıl önce

A Zimbabwean merchant using crutches walked normally at the 139th Canton Fair -- thanks to a wearable exoskeleton. He bought it on the spot and now aims to bring the technology to Africa. Innovation meets opportunity. #CantonFair #exoskeleton #robotics
0:14

A Zimbabwean merchant using crutches walked normally at the 139th Canton Fair -- thanks to a wearable exoskeleton. He bought it on the spot and now aims to bring the technology to Africa. Innovation meets opportunity. #CantonFair #exoskeleton #robotics

China Xinhua News

77,241 görüntüleme • 2 ay önce

Can we robustly track an object’s 6D pose in contact-rich, occluded scenarios? Yes! Our solution, V-HOP, fuses vision and touch through a visuo-haptic transformer for precise, real-time tracking. arXiv: Project:
0:13

Can we robustly track an object’s 6D pose in contact-rich, occluded scenarios? Yes! Our solution, V-HOP, fuses vision and touch through a visuo-haptic transformer for precise, real-time tracking. arXiv: Project:

Hongyu Li

37,162 görüntüleme • 1 yıl önce

🚀 Introducing Frank 🤖—a whole-body robot control system for day-to-day household chores. Frank has been in works for the past year—a tightly coupled hardware and a remote teleoperation interface co-led by Michelle, Nandini, and Pulkit Agrawal. Thanks to Aditya Agarwal and Shankara Narayanan for the awesome help with recording the videos! 🎥 🧵[1/6]
0:14

🚀 Introducing Frank 🤖—a whole-body robot control system for day-to-day household chores. Frank has been in works for the past year—a tightly coupled hardware and a remote teleoperation interface co-led by Michelle, Nandini, and Pulkit Agrawal. Thanks to Aditya Agarwal and Shankara Narayanan for the awesome help with recording the videos! 🎥 🧵[1/6]

Bipasha Sen

51,130 görüntüleme • 1 yıl önce

Meet #HillbotAlpha, the first fully autonomous mobile manipulation robot trained using sim-to-real technology. Designed in Hillbot’s San Diego headquarters, Hillbot Alpha represents the potential of data synthesis via simulation in robotics. Through a robust strategy that combines a small sample of real-world data with synthetic data, Hillbot Alpha can effectively adapt to evolving task requirements and environments while working safely beside humans. #Hillbot #AGI #EmbodiedAI #AI #Sim2Real #ArtificialIntelligence #Simulation #Simtoreal
0:11

Meet #HillbotAlpha, the first fully autonomous mobile manipulation robot trained using sim-to-real technology. Designed in Hillbot’s San Diego headquarters, Hillbot Alpha represents the potential of data synthesis via simulation in robotics. Through a robust strategy that combines a small sample of real-world data with synthetic data, Hillbot Alpha can effectively adapt to evolving task requirements and environments while working safely beside humans. #Hillbot #AGI #EmbodiedAI #AI #Sim2Real #ArtificialIntelligence #Simulation #Simtoreal

Hao Su

13,821 görüntüleme • 1 yıl önce

A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.
0:15

A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.

Rerun

21,760 görüntüleme • 2 yıl önce