Like something out of the Addams Family, scientists have created a detachable robotic hand that can crawl and grab objects. The design enables tasks such as retrieving objects beyond normal reach and performing multi-object handling, offering potential applications in industrial, service, and exploratory robotics.
By rethinking how thin metal threads are woven into a flexible textile, EPFL researchers have created a lightweight fabric capable of lifting over 400 times its own weight. The work advances the development of wearables that provide physical assistance without mechanical bulk.
To reliably complete household chores, assemble products and tackle other manual tasks, robots should be able to adapt their manipulation strategies based on the objects they are working with, similarly to how humans leverage information they gain via the sense of touch. While humans attain tactile information via nerves in their skin and muscles, robots rely on sensors, devices that sense their surroundings and pick up specific physical signals.
The advancement of artificial intelligence (AI) algorithms has opened new possibilities for the development of robots that can reliably tackle various everyday tasks. Training and evaluating these algorithms, however, typically requires extensive efforts, as humans still need to manually label training data and assess the performance of models in both simulations and real-world experiments.
Almost half of our attention during face-to-face conversation focuses on lip motion. Yet, robots still struggle to move their lips correctly. Even the most advanced humanoids make little more than muppet mouth gestures—if they have a face at all.
Underwater robots face many challenges before they can truly master the deep, such as stability in choppy currents. A new paper published in the journal npj Robotics provides a comprehensive update of where the technology stands today, including significant progress inspired by the movement of rays.
Despite rapid robotic automation advancements, most systems struggle to adapt their pre-trained movements to dynamic environments with objects of varying stiffness or weight. To tackle this challenge, researchers from Japan have developed an adaptive motion reproduction system using Gaussian process regression.
When Elon Musk talks about robotics, he rarely hides the ambition behind the dream.
When Aaron Tan began his Ph.D. in mechanical and industrial engineering at the University of Toronto in 2019, leading a robotics startup in Silicon Valley was the furthest thing from his mind.
Two robots the size of schoolchildren stepped into the ring at BattleBots Arena.
While the capabilities of robots have improved significantly over the past decades, they are not always able to reliably and safely move in unknown, dynamic and complex environments. To move in their surroundings, robots rely on algorithms that process data collected by sensors or cameras and plan future actions accordingly.
Humanoid robots danced, somersaulted, dealt blackjack and played ping-pong at the Consumer Electronics Show this week, but some in the industry are impatient for them to become more useful, not just a promise of things to come.
A collaboration between Princeton University engineers and entomologists at the University of Illinois Urbana-Champaign began with the researchers chasing grasshoppers in a hot parking lot. Their eventual focus on the hindwings of one species of grasshopper, Schistocerca americana, the American grasshopper, is inspiring a new approach to untethered gliding flight.
Hyundai-owned Boston Dynamics publicly demonstrated its humanoid robot Atlas for the first time Monday at the CES tech showcase, ratcheting up a competition with Tesla and other rivals to build robots that look like people and do things that people do.
Researchers at National Taiwan University have developed an AI system that recognizes construction activities at both the individual and crew levels using ordinary site videos. The approach reveals how teamwork shapes productivity and provides a foundation for future human–robot collaboration on construction sites.