Archive 02.05.2024

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.

Visitors will have the chance to see the entire THE robot range, including the THE400, THE600, THE800 and THE1000. The line-up includes robots of different sizes and strengths, providing visitors with an opportunity to find the perfect model for their application.

TRS has been active with A3 for years, and as the Detroit show proved --- there are tremendous opportunities at Automate.

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.