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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 görüntüleme • 9 ay önce •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.

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Researchers developed a system called LATENT that teaches humanoid robots to play tennis using small pieces of human movement data. It learns basic motion fragments, then corrects and combines them to create natural movements and swings. The system was tested on the Unitree G1 humanoid robot, enabling it to hit balls and rally with human players.

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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.

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.

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.

Lukas Ziegler

17,259 görüntüleme • 1 yıl önce

A robot snake for the rescue! 🛟 Mechanum wheels, reconfigurable shape, camera set. These robots were developed by researchers at The University of Manchester. This machine is called MIRRAX-150, and it performs inspections as well as explorations. To map 3D point clouds, this machine uses low-cost components, 3D printing, and LiDAR technology. 🧭 Soon, this robot will be deployed on the Sellafield nuclear power station. Basically, it's a snake that changes its body! P.S. What use cases would fit these robots? 🐍 ~~~ ♻️ RT to help 1 robot find a new workplace.

A robot snake for the rescue! 🛟 Mechanum wheels, reconfigurable shape, camera set. These robots were developed by researchers at The University of Manchester. This machine is called MIRRAX-150, and it performs inspections as well as explorations. To map 3D point clouds, this machine uses low-cost components, 3D printing, and LiDAR technology. 🧭 Soon, this robot will be deployed on the Sellafield nuclear power station. Basically, it's a snake that changes its body! P.S. What use cases would fit these robots? 🐍 ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

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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:

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.

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.

Lukas Ziegler

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🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

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New mobility for robots! 🛹 The Collinear Mecanum Drive (CMD) is a novel robot locomotion system that combines omnidirectional motion with dynamic balancing. The robot in the video was developed at The University of Sheffield by Matthew Watson. By using three or more collinear Mecanum wheels, the CMD enables robots to move in any direction while maintaining a narrow ground footprint, limited only by wheel diameter. ⚙️ This allows robots to navigate through tighter spaces compared to traditional omnidirectional systems by moving directly along the axis of wheel rotation. Additionally, the CMD’s dynamic balancing capability permits taller robot designs without needing a wider base to prevent tipping during movement or external disturbances. ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

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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.

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.

The Humanoid Hub

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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! 🦾

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! 🦾

Lukas Ziegler

27,548 görüntüleme • 1 yıl önce

Tomorrow’s Science, Today’s Robot A humanoid robot is a type of robot designed to look and act like a human being. It usually has a body structure with a head, torso, arms, and legs, and may be programmed to walk, talk, or perform tasks similar to humans. Some humanoid robots are made mainly for research and testing, while others are used in healthcare, customer service, education, or entertainment. Advanced ones even use artificial intelligence (AI) to recognise speech, facial expressions, and interact naturally with people.

Tomorrow’s Science, Today’s Robot A humanoid robot is a type of robot designed to look and act like a human being. It usually has a body structure with a head, torso, arms, and legs, and may be programmed to walk, talk, or perform tasks similar to humans. Some humanoid robots are made mainly for research and testing, while others are used in healthcare, customer service, education, or entertainment. Advanced ones even use artificial intelligence (AI) to recognise speech, facial expressions, and interact naturally with people.

NTANetwork

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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 →

Lukas Ziegler

28,012 görüntüleme • 1 ay 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.

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

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🚨BREAKING: A new humanoid robot straight out of Europe! Kinisi has introduced their humanoid robot called KR1. This robot has a base with wheels instead of legs. This provides much greater stability and safety for human workers. It’s built for warehouse and factory tasks like picking, stacking, and moving goods. It supports payloads up to 15 kg, runs 8 hours per charge with hot-swappable batteries, and docks itself for recharging. The robot can lift heavy boxes from high shelves, handle fragile items without damage, and adapt its arm trajectories on the fly to avoid obstacles. In demos, it moved smoothly between shelves, placed items into totes, and transported them across the warehouse, all autonomously. Great retrofitting potential, because it doesn’t require major infrastructure changes to deploy. It learns from simple demonstrations, operates offline for reliability and privacy, and integrates quickly into existing workflows. Let’s not forget that E-commerce is growing rapidly, with global online sales expected to reach nearly $7 trillion by 2025. As more people shop online, the number of packages being shipped each day is soaring. These robots help us to make sure they will be delivered on time! 📦

🚨BREAKING: A new humanoid robot straight out of Europe! Kinisi has introduced their humanoid robot called KR1. This robot has a base with wheels instead of legs. This provides much greater stability and safety for human workers. It’s built for warehouse and factory tasks like picking, stacking, and moving goods. It supports payloads up to 15 kg, runs 8 hours per charge with hot-swappable batteries, and docks itself for recharging. The robot can lift heavy boxes from high shelves, handle fragile items without damage, and adapt its arm trajectories on the fly to avoid obstacles. In demos, it moved smoothly between shelves, placed items into totes, and transported them across the warehouse, all autonomously. Great retrofitting potential, because it doesn’t require major infrastructure changes to deploy. It learns from simple demonstrations, operates offline for reliability and privacy, and integrates quickly into existing workflows. Let’s not forget that E-commerce is growing rapidly, with global online sales expected to reach nearly $7 trillion by 2025. As more people shop online, the number of packages being shipped each day is soaring. These robots help us to make sure they will be delivered on time! 📦

Lukas Ziegler

23,262 görüntüleme • 9 ay önce

iRonCub3 is the world’s first jet-powered humanoid robot developed by researchers at the Italian Institute of Technology. It uses four jet engines that allow it to fly and handle complex missions. The robot is designed for extreme conditions and could help in disaster relief by reaching hard-to-access areas.

iRonCub3 is the world’s first jet-powered humanoid robot developed by researchers at the Italian Institute of Technology. It uses four jet engines that allow it to fly and handle complex missions. The robot is designed for extreme conditions and could help in disaster relief by reaching hard-to-access areas.

Space and Technology

23,608 görüntüleme • 2 ay önce

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

Lukas Ziegler

25,990 görüntüleme • 9 ay önce