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Dynamic manipulation steps up to the plate! This is a first look at a low-impedance platform designed to study how robots manipulate objects. In this demo, two robots play catch and practice batting, even teaming up with humans. The robots are capable of throwing 70mph [112 kph] and can...

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RAI Institute

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25,497 görüntüleme • 9 ay önce •via X (Twitter)

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Robots Using Their Feet to Manipulate Objects ETH Zurich researchers have advanced "pedipulation," where legged robots use their feet for manipulation instead of robotic arms. Their new approach trains robots to navigate and manipulate objects while avoiding static and dynamic obstacles. Tested on the ANYmal quadruped, the method enables precise foot control in complex, unseen environments after minimal training in simulation. Will this research open up new possibilities for efficient, obstacle-aware robot manipulation?
1:04

Robots Using Their Feet to Manipulate Objects ETH Zurich researchers have advanced "pedipulation," where legged robots use their feet for manipulation instead of robotic arms. Their new approach trains robots to navigate and manipulate objects while avoiding static and dynamic obstacles. Tested on the ANYmal quadruped, the method enables precise foot control in complex, unseen environments after minimal training in simulation. Will this research open up new possibilities for efficient, obstacle-aware robot manipulation?

Circuit

11,802 görüntüleme • 1 yıl önce

Drop the camera, and bang -- the robots do the job without camera calibration or additional data collection. We want robots to manipulate anywhere! Our new work, **Maniwhere**, enables robots to manipulate objects from any camera view with any background. Isn't this the generalization every roboticist is looking for? How do we achieve this with sim2real? You can check this thread from Zhecheng Yuan ! And the website is here:
0:29

Drop the camera, and bang -- the robots do the job without camera calibration or additional data collection. We want robots to manipulate anywhere! Our new work, **Maniwhere**, enables robots to manipulate objects from any camera view with any background. Isn't this the generalization every roboticist is looking for? How do we achieve this with sim2real? You can check this thread from Zhecheng Yuan ! And the website is here:

Huazhe Harry Xu

27,564 görüntüleme • 2 yıl önce

Robots can bake bread, cut carrots, and do karate kicks in demo videos. But how capable are they, really? Robocurve PBC measures how good AI and robots are in the physical world, from making a sandwich to building data centers.
1:37

Robots can bake bread, cut carrots, and do karate kicks in demo videos. But how capable are they, really? Robocurve PBC measures how good AI and robots are in the physical world, from making a sandwich to building data centers.

Jay Chooi

42,347 görüntüleme • 1 ay önce

NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:
2:34

NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:

Dave Lee

372,039 görüntüleme • 3 yıl önce

This is not a 2050 warehouse. This is a modern air hub of Amazon. Amazon uses robots to help sort and ship billions of packages each year. At Amazon Air, the company's main air hub in Kentucky, hundreds of self-charging robots zoom across the warehouse floor to organize orders. These robots, called “drives,” follow small stickers on the floor to know where to go and when to speed up or slow down. Amazon has more than ONE MILLION robots in its facilities, and it’s even deploying more new types of robots that can sort items of different shapes and sizes. P.S. Can you guess how many robots they had back in 2013? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:23

This is not a 2050 warehouse. This is a modern air hub of Amazon. Amazon uses robots to help sort and ship billions of packages each year. At Amazon Air, the company's main air hub in Kentucky, hundreds of self-charging robots zoom across the warehouse floor to organize orders. These robots, called “drives,” follow small stickers on the floor to know where to go and when to speed up or slow down. Amazon has more than ONE MILLION robots in its facilities, and it’s even deploying more new types of robots that can sort items of different shapes and sizes. P.S. Can you guess how many robots they had back in 2013? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

178,090 görüntüleme • 1 ay önce

Researchers at RAI Institute developed the Koala gripper to help robots handle tools and objects more naturally. The system includes a handheld version for humans and a motorized version for robots, both using the same design and sensors. By learning from human demonstrations recorded with cameras and force sensors, robots can repeat the same tasks using the Koala gripper.
0:24

Researchers at RAI Institute developed the Koala gripper to help robots handle tools and objects more naturally. The system includes a handheld version for humans and a motorized version for robots, both using the same design and sensors. By learning from human demonstrations recorded with cameras and force sensors, robots can repeat the same tasks using the Koala gripper.

Space and Technology

21,980 görüntüleme • 2 ay önce

🤖ROBOTS ARE GETTING SMARTER AT TOUCHING THE REAL WORLD Researchers from UC Berkeley, NVIDIA and Stanford introduced T-Rex, a framework that combines vision, language and tactile sensing. Instead of relying on cameras alone, robots can now respond to physical contact in real time. Robots are no longer just seeing objects. They’re learning how to FEEL them.
2:22

🤖ROBOTS ARE GETTING SMARTER AT TOUCHING THE REAL WORLD Researchers from UC Berkeley, NVIDIA and Stanford introduced T-Rex, a framework that combines vision, language and tactile sensing. Instead of relying on cameras alone, robots can now respond to physical contact in real time. Robots are no longer just seeing objects. They’re learning how to FEEL them.

Coin Bureau

28,275 görüntüleme • 2 ay önce

Robotics Expert on How Robots Learn: At First Much Slower Than Humans, Then Infinitely Scalable Agility Jonathan Hurst: “I don't believe that there's a singularity. I do believe that things are going to get better and better. Think of it more like a snowball picking up steam going down a hill. But the reason that it's snowballing like this is because people are putting money, and resources, and engineering time, and engineering effort in as they explore everything and start to figure all of this stuff out. Humans, for example, we've evolved to learn. We are very good at learning, and it takes very little data to show us how to do something, and then we practice and practice, iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples than a person. We're still figuring out how to teach robots how to learn. But one of the benefits that robots have in the long run is they have Wi-Fi. When you learn how to play the violin, you can't just load that to somebody else and then they know how to play the violin based on your learnings. Robots will be able to do that. @jason: “One robot learns to play violin, all robots know how to play a violin.” Jonathan Hurst: “Or all robots of that type know how to play the violin, right? And then minor variations for the next type and the next piece of hardware.” ------------------------------ Thanks to our partners for making this possible! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads. 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Now open to everyone. Sign up at If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates. Check out Plaud at and use code ALLIN for up to 10% off! Which is also available on Amazon: (Code: ALLIN10X)
0:54

Robotics Expert on How Robots Learn: At First Much Slower Than Humans, Then Infinitely Scalable Agility Jonathan Hurst: “I don't believe that there's a singularity. I do believe that things are going to get better and better. Think of it more like a snowball picking up steam going down a hill. But the reason that it's snowballing like this is because people are putting money, and resources, and engineering time, and engineering effort in as they explore everything and start to figure all of this stuff out. Humans, for example, we've evolved to learn. We are very good at learning, and it takes very little data to show us how to do something, and then we practice and practice, iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples than a person. We're still figuring out how to teach robots how to learn. But one of the benefits that robots have in the long run is they have Wi-Fi. When you learn how to play the violin, you can't just load that to somebody else and then they know how to play the violin based on your learnings. Robots will be able to do that. @jason: “One robot learns to play violin, all robots know how to play a violin.” Jonathan Hurst: “Or all robots of that type know how to play the violin, right? And then minor variations for the next type and the next piece of hardware.” ------------------------------ Thanks to our partners for making this possible! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads. 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Now open to everyone. Sign up at If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates. Check out Plaud at and use code ALLIN for up to 10% off! Which is also available on Amazon: (Code: ALLIN10X)

The All-In Podcast

70,364 görüntüleme • 15 gün önce

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this? Baoxiong Jia tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP. To learn more, watch Episode #49 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton.
50:15

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this? Baoxiong Jia tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP. To learn more, watch Episode #49 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton.

RoboPapers

44,813 görüntüleme • 8 ay önce

Apple gets it. Robots are going to be everywhere, but they won’t look like robots. Check out their new paper ELEGNT. I believe this is the future of everyday objects: helpful and human.
4:16

Apple gets it. Robots are going to be everywhere, but they won’t look like robots. Check out their new paper ELEGNT. I believe this is the future of everyday objects: helpful and human.

Nacho Mellado

1,033,144 görüntüleme • 1 yıl önce

Cooking in kitchens is fun. BUT doing it collaboratively with two robots is even more satisfying! We introduce MOSAIC, a modular framework that coordinates multiple robots to closely collaborate and cook with humans via natural language interaction and a repository of skills.
1:14

Cooking in kitchens is fun. BUT doing it collaboratively with two robots is even more satisfying! We introduce MOSAIC, a modular framework that coordinates multiple robots to closely collaborate and cook with humans via natural language interaction and a repository of skills.

Sanjiban Choudhury

26,423 görüntüleme • 2 yıl önce

Robots with tentacles! 🐙 SpiRobs are soft robots inspired by nature, designed in the shape of a logarithmic spiral. They mimic the flexible movements of octopus arms and elephant trunks to grasp objects. 🐘 SpiRobs use simple cables to move, making them easy to control and operate. They can scale from small to large sizes, ranging from centimeters to meters. The robots can adapt to different object shapes, improving their grasping ability. A mini version can act as a tiny gripper, while a large version can be mounted on drones. Interestingly, multiple robots can work together to wrap around and hold large objects. That's just out of this world! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:04

Robots with tentacles! 🐙 SpiRobs are soft robots inspired by nature, designed in the shape of a logarithmic spiral. They mimic the flexible movements of octopus arms and elephant trunks to grasp objects. 🐘 SpiRobs use simple cables to move, making them easy to control and operate. They can scale from small to large sizes, ranging from centimeters to meters. The robots can adapt to different object shapes, improving their grasping ability. A mini version can act as a tiny gripper, while a large version can be mounted on drones. Interestingly, multiple robots can work together to wrap around and hold large objects. That's just out of this world! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

121,386 görüntüleme • 8 ay önce

World Simulator, reimagined — now alive with humans, robots, and their vibrant society unfolding in 3D real-world geospatial scenes across the globe! 🚀 One day soon, humans and robots will co-exist in the same world. To prepare, we must address: 1️⃣ How can robots cooperate or compete intelligently? 2️⃣ How do humans build social bonds and communities? 3️⃣ How can both co-exist in an open, dynamic world? Announcing Virtual Community Project — a social-physical world simulator, where human characters and robotic agents can interact, grow, and co-evolve within open-world societies, stretching from London to New York, and beyond! Key features include: ✅ Unified multi-agent physics simulations for rich social + physical interactions of humans and robots ✅ Massive auto-generated 3D scenes grounded with the rea-world geospatial data ✅ Agent communities populated by robots and LLM-driven human characters with rich appearances, personalities, and social ties. 🌍 Enter our Virtual Community, an open world to study embodied AI at scale— one social-physical world model at a time! 🔗 Project: 💻 Code: Paper: 1/n
0:53

World Simulator, reimagined — now alive with humans, robots, and their vibrant society unfolding in 3D real-world geospatial scenes across the globe! 🚀 One day soon, humans and robots will co-exist in the same world. To prepare, we must address: 1️⃣ How can robots cooperate or compete intelligently? 2️⃣ How do humans build social bonds and communities? 3️⃣ How can both co-exist in an open, dynamic world? Announcing Virtual Community Project — a social-physical world simulator, where human characters and robotic agents can interact, grow, and co-evolve within open-world societies, stretching from London to New York, and beyond! Key features include: ✅ Unified multi-agent physics simulations for rich social + physical interactions of humans and robots ✅ Massive auto-generated 3D scenes grounded with the rea-world geospatial data ✅ Agent communities populated by robots and LLM-driven human characters with rich appearances, personalities, and social ties. 🌍 Enter our Virtual Community, an open world to study embodied AI at scale— one social-physical world model at a time! 🔗 Project: 💻 Code: Paper: 1/n

Chuang Gan

90,450 görüntüleme • 1 yıl önce