A first-ever stretchy electronic skin could equip robots and other devices with the same softness and touch sensitivity as human skin, opening up new possibilities to perform tasks that require a great deal of precision and control of force.
Researchers at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) have successfully leveraged robotic assistance in the manufacture of wind turbine blades, allowing for the elimination of difficult working conditions for humans and the potential to improve the consistency of the product.
Researchers have successfully leveraged robotic assistance in the manufacture of wind turbine blades, allowing for the elimination of difficult working conditions for humans and the potential to improve the consistency of the product.
New algorithm encourages robots to move more randomly to collect more diverse data for learning. In tests, robots started with no knowledge and then learned and correctly performed tasks within a single attempt. New model could improve safety and practicality of self-driving cars, delivery drones and more.
With Tacton we wanted to give users the flexibility and configuration options they need to outfit their facility, while ensuring the system was reliable, secure, and easy to install.
Northwestern University engineers have developed a new artificial intelligence (AI) algorithm designed specifically for smart robotics. By helping robots rapidly and reliably learn complex skills, the new method could significantly improve the practicality—and safety—of robots for a range of applications, including self-driving cars, delivery drones, household assistants and automation.
Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions—essentially, high-level representations of complex concepts that skip less-important details—and thus sputter when asked to do more sophisticated tasks.
CART will learn from only a few examples in an interactive manner by actively prompting the caregiver for demonstration examples as the robot needs, and thereby not overly burdening the caregiver.
CART will learn from only a few examples in an interactive manner by actively prompting the caregiver for demonstration examples as the robot needs, and thereby not overly burdening the caregiver.
You wanna see her move? I think that's the fun part.
Robots have already proved to be promising tools to complete complex and demanding maintenance tasks. While engineers have developed a wide range of robots that could help to maintain and repair infrastructure, many of these robots need to be plugged into external power sources, which limits their real-world application.
One group commonly misunderstood by voice technology are individuals who speak African American English, or AAE. Researchers designed an experiment to test how AAE speakers adapt their speech when imagining talking to a voice assistant, compared to talking to a friend, family member, or stranger. The study tested familiar human, unfamiliar human, and voice assistant-directed speech conditions by comparing speech rate and pitch variation. Analysis of the recordings showed that the speakers exhibited two consistent adjustments when they were talking to voice technology compared to talking to another person: a slower rate of speech with less pitch variation.
Over the past decade, researchers all around the world have been finding new and exciting use cases for unmanned aerial vehicles (UAVs). Commonly called "drones," UAVs have proved their worth across many fields, including photography, agriculture, land surveying, disaster management, and even the transportation of goods.
A four-legged robot trained with machine learning has learned to avoid falls by spontaneously switching between walking, trotting, and pronking -- a milestone for roboticists as well as biologists interested in animal locomotion.
A four-legged robot trained with machine learning by EPFL researchers has learned to avoid falls by spontaneously switching between walking, trotting, and pronking—a milestone for roboticists as well as biologists interested in animal locomotion.