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Robot dog learned dexterity! 🤯 Robots usually struggle to move and handle objects at the same time, a challenge called loco-manipulation. Researchers at the RAI Institute have developed a new system called ReLIC that makes this easier. Instead of giving robots fixed roles for arms and legs, ReLIC lets... show more

Lukas Ziegler

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19,743 просмотров • 9 месяцев назад •via X (Twitter)

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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.

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.

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Researchers have developed Humanoid-GPT, a new AI system that helps humanoid robots perform complex movements and tasks. Tested on the Unitree G1 robot, it enables real-time whole-body control and can perform new actions without special training. During demonstrations, the robot successfully carried out a variety of movements and tasks, showing strong potential for real-world applications.

Researchers have developed Humanoid-GPT, a new AI system that helps humanoid robots perform complex movements and tasks. Tested on the Unitree G1 robot, it enables real-time whole-body control and can perform new actions without special training. During demonstrations, the robot successfully carried out a variety of movements and tasks, showing strong potential for real-world applications.

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This is Delta, a deformable multirotor robot developed by scientists at the University of Tokyo that can both fly and roll using its flexible body design. It uses a thrust control system to move efficiently in the air and on the ground, allowing it to adapt to different environments. This capability makes it useful for tasks like disaster response and long-duration operations, introduced in 2024.

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A mobile robot that can fly! 🐝 DoubleBee is a new robot developed at Nanyang Technological University Singapore that can both fly and move on the ground, using two tilting propellers and two wheels. In the air, it flies efficiently like a bicopter, and on the ground, it balances like a two-wheeled robot, saving energy. On the ground, the propellers help control its balance while the wheels handle movement, making control easier. The robot is built with off-the-shelf parts and tested in real-world experiments to prove its efficiency and performance. DoubleBee can navigate tough environments, fly over obstacles, crawl under barriers, and climb rough slopes.

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Teaching robots real dexterity has always been a challenge. But what if they could handle tools like a human? DexterityGen (DexGen) is a new system that helps robots use their hands better. It improves how they grip, move, and handle objects… from holding a pen to using a screwdriver. DexGen learns in simulation and refines its skills in the real world, making robotic hands much more useful. What makes DexGen special? ✅ Smarter movements that refine rough actions into precise skills ✅ Trained on a massive collection of dexterous tasks for better learning ✅ Better teleoperation that makes robotic hand control easier and safer ✅ Handles real-world challenges like small objects, tricky angles, and gravity This moves robots closer to real dexterity. It makes tool use more natural, improves stability, and brings robotic hands one step closer to human-level skill. Seen at Zhao-Heng Yin 🫶 Github: Paper:

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A robot with wheels and legs! 🦿 Wheeled mobile robots are popular in many fields because they are stable, efficient, and simple to build. However, they often occupy more space than humans and struggle to navigate crowded hallways, ramps, or elevators. 🛗 To address this, a robot developed at the Korea University of Technology and Education combines legs for extra support and mecanum wheels for smooth, all-directional movement. It has a slim, human-like design with four wheel-leg mechanisms, each using a special 2-DOF leg and a mecanum wheel. A unique mechanism keeps the wheels steady while the legs move, helping the robot stay agile and absorb shocks. The design also reduces strain on the motors, making it more efficient. Tests showed that the robot could move quickly, climb steps, and navigate elevators with people. Research paper in the comments! 🗞️ ~~~ ♻️ R to help 1 robot find a new workplace.

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TextOp: A real-time text-to-motion framework for humanoid robots. It allows users to instruct the robot using natural language and to modify commands on the fly, producing smooth, whole-body motions instantly.

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Changing a light bulb with a robot! 💡 Researchers at the Korea Institute of Machinery & Materials developed a gripper that can handle complex manipulation tasks like bulb and bolt installation. It has three fingers with nine degrees of freedom (9 DoF), allowing precise control. Each finger moves using a special linkage system that enables flexible motion. The gripper can grasp objects of different shapes and adjust their positions accurately. Tests showed that it can lift up to 12 kg and apply a fingertip force of 40 N with high precision. Complex manipulation can be handled by robots! 🦾

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Teaching robots to work smoothly with humans! 👀 Soon enough, a robot will be standing in the garage holding a flashlight for your dad, and doing a better job of it than you ever did. 🔦 Carnegie Mellon University and The University of Texas at Arlington researchers introduced HALO, a new method for robots to collaborate better with humans. When robots work with humans, they face unpredictable behavior. Every person moves differently, makes different decisions, and reacts differently to the robot. Traditional learning approaches struggle because the robot and human are fundamentally different, they think and move in completely different ways. This mismatch causes the robot's learning to become unstable, like trying to balance on a wobbly chair. Mentioned research solves this by mathematically guaranteeing the robot's learning stays stable. Pushing objects together in sync, transporting items through tight spaces, and handling super-long objects that require coordination. Real humanoid-robot experiments show HALO handles tricky collaborative situations much better. How it works? It basically has 3 layers. Vision layer understands what needs to happen by looking at the scene. Coordination layer figures out how the robot and human should work together tactically. Control layer executes the movements with fast, stable whole-body control. 📜 Paper website: Congrats Ding Zhao + all co-authors! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Teaching robots to work smoothly with humans! 👀 Soon enough, a robot will be standing in the garage holding a flashlight for your dad, and doing a better job of it than you ever did. 🔦 Carnegie Mellon University and The University of Texas at Arlington researchers introduced HALO, a new method for robots to collaborate better with humans. When robots work with humans, they face unpredictable behavior. Every person moves differently, makes different decisions, and reacts differently to the robot. Traditional learning approaches struggle because the robot and human are fundamentally different, they think and move in completely different ways. This mismatch causes the robot's learning to become unstable, like trying to balance on a wobbly chair. Mentioned research solves this by mathematically guaranteeing the robot's learning stays stable. Pushing objects together in sync, transporting items through tight spaces, and handling super-long objects that require coordination. Real humanoid-robot experiments show HALO handles tricky collaborative situations much better. How it works? It basically has 3 layers. Vision layer understands what needs to happen by looking at the scene. Coordination layer figures out how the robot and human should work together tactically. Control layer executes the movements with fast, stable whole-body control. 📜 Paper website: Congrats Ding Zhao + all co-authors! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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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.

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.

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A robot from NASA with a bionic hand! 🧑🏼‍🚀 Giving a humanoid more dexterity with a PSYONIC ability hand! NASA Valkyrie robot, standing at 1.8 meters tall and weighing 125 kg, is a bipedal humanoid designed to perform tasks in dangerous environments on Earth and in space. Valkyrie is part of NASA's efforts to enhance remote control technologies and develop fully autonomous robots. 🕹️ The goal is to allow astronauts to supervise hazardous work remotely and delegate repetitive tasks to robots. This approach can help them work on more complex tasks, even in locations where astronauts are not physically present. P.S. NASA plans to deploy Valkyrie on the Moon for long-term work and habitats during Artemis missions, even without astronauts.

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