Engineers at Princeton and North Carolina State University have combined ancient paper-folding and modern materials science to create a soft robot that bends and twists through mazes with ease.
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.
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.
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.
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 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.
Prof. Angela Schoellig from the Technical University of Munich (TUM) uses ChatGPT to develop choreographies for swarms of drones to perform along to music. An additional safety filter prevents mid-air collisions. The researcher's results demonstrate the first time that large language models (LLMs) such as ChatGPT can be used in robotics.
The development and testing of algorithms for robotics applications typically requires evaluations in both simulated and physical environments. Some algorithms, however, can be difficult to deploy in simple hardware experiments, due to the high costs of robotics hardware or to difficulties associated with setting up this hardware inside robotics labs. Moreover, often developers lack reliable software that would allow them to integrate their algorithms on a specific robotics platform.
Over the past decades, dozens of animal species have become extinct, while thousands of others are now at risk of disappearing. Endangered species include various pollinators, including bees and some types of moths, butterflies, and flies.
If instructed to "Place a cooled apple into the microwave," how would a robot respond? Initially, the robot would need to locate an apple, pick it up, find the refrigerator, open its door, and place the apple inside. Subsequently, it would close the refrigerator door, reopen it to retrieve the cooled apple, pick up the apple again, and close the door. Following this, the robot would need to locate the microwave, open its door, place the apple inside, and then close the microwave door.
Creating robots to safely aid disaster victims is one challenge; executing flexible robot control that takes advantage of the material's softness is another. The use of pliable soft materials to collaborate with humans and work in disaster areas has drawn much recent attention. However, controlling soft dynamics for practical applications has remained a significant challenge.
A remotely operated underwater robot built by a team of Rice University engineering students pioneers a new way to control buoyancy via water-splitting fuel cells. The device, designed and constructed at the Oshman Engineering Design Kitchen over the course of a year-long senior design capstone class, offers a more power-efficient method of maintaining neutral buoyancy—a critical component in underwater operations.