This interdisciplinary program synergizes science, engineering, information systems and computing, focusing on manufacturing's next evolutionary step called Industry 4.0. The new facility ecosystem also has wide-ranging applications, opening up potential curriculum expansion.
In a step toward more autonomous soft robots and wearable technologies, EPFL researchers have created a device that uses color to simultaneously sense multiple mechanical and temperature stimuli.
Researchers at Tufts University and Harvard University's Wyss Institute have created tiny biological robots that they call Anthrobots from human tracheal cells that can move across a surface and have been found to encourage the growth of neurons across a region of damage in a lab dish.
A team of engineers at Beihang University, working with a colleague from Tsinghua University, both in China, has designed, built and tested a haptically controlled octopus robot arm that is capable of grasping, lifting and carrying objects on land and underwater. In an article published in the journal Science Robotics, the group describes how they built their robot, how it works and how well it performed when tested under a variety of scenarios.
A smooth production process is critical for a company’s success. The constantly growing demands of the market and increasing cost pressure require innovative solutions to optimize production. This is where transfer systems come into play.
Modern robots know how to sense their environment and respond to language, but what they don't know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.
An inspection design method and procedure by which mobile robots can inspect large pipe structures has been demonstrated with the successful inspection of multiple defects on a three-meter long steel pipe using guided acoustic wave sensors.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
We share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.