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.
If you accidentally put your hand on a hot object, you'll naturally pull it away fast, before you have to think about it. This happens thanks to sensory nerves in your skin that send a lightning-fast signal to your spinal cord, which immediately activates your muscles. The speed at which this happens helps prevent serious burns. Your brain is only informed once the movement has already started.
Beneath the moon's cratered surface lie networks of lava tubes and deep pits, natural caves that could shelter future lunar bases from cosmic radiation and wild temperature swings. These underground structures represent some of the most scientifically valuable areas in the solar system, but they come with the very real challenge of simply getting there.
Beneath the moon's cratered surface lie networks of lava tubes and deep pits, natural caves that could shelter future lunar bases from cosmic radiation and wild temperature swings. These underground structures represent some of the most scientifically valuable areas in the solar system, but they come with the very real challenge of simply getting there.
Researchers at the University of Pennsylvania and University of Michigan have created the world's smallest fully programmable, autonomous robots: microscopic swimming machines that can independently sense and respond to their surroundings, operate for months and cost just a penny each.
Robots are becoming part of our everyday lives, from health care to home assistance. But for humans to truly trust and collaborate with them, robots need more than technical skill—they need to understand us.
From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of these environments lack natural or artificial light, making it difficult for robotic systems, which usually rely on cameras and vision algorithms, to operate effectively.