An international group of researchers has created a new approach to imitating human motion by combining central pattern generators (CPGs) and deep reinforcement learning (DRL). The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adaptation to environments with unstable surfaces.
Getting robots to perform even a simple task requires a great deal of behind-the-scenes work. Part of the challenge is planning and executing movements, everything from turning wheels to lifting a robotic arm. To make this happen, roboticists collaborate with programmers to develop a set of trajectories—or pathways—that are clear of obstacles and doable for the robot.
When old food packaging, discarded children's toys and other mismanaged plastic waste break down into microplastics, they become even harder to clean up from oceans and waterways. These tiny bits of plastic also attract bacteria, including those that cause disease.
A team of roboticists at the Chinese University of Hong Kong has created a robot snail with a helmet-like shell that moves by rolling around on bulldozer-like tracks. They have published a paper on their research in Nature Communications.
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.