Many products in the modern world are in some way fabricated using computer numerical control (CNC) machines, which use computers to automate machine operations in manufacturing. While simple in concept, the ways to instruct these machines is in reality often complex. A team of researchers has devised a system to demonstrate how to mitigate some of this complexity.
Claire chatted to Miranda Lowther from the University of Bristol about soft, sensitive electronic skin for prosthetic limbs.
Miranda Lowther is a PhD researcher at the FARSCOPE-TU Centre for Doctoral Training, a joint venture between University of Bristol, University of West of England, and Bristol Robotics Laboratory, where she is pursuing her passion for using soft robotics and morphological computation to help people in healthcare. For her PhD, she is investigating how soft e-skins and morphological computation concepts can be used to improve prosthetic user health, comfort, and quality of life, through sensing and adaptation.
At Automate 2025, the company will display all four models from the range: the THE400, THE600, THE800 and THE1000. The compact THE400 is ideal for fast, precise operations in assembly and inspection processes, particularly within the electronics and automotive sectors.
Humans are better than current AI models at interpreting social interactions and understanding social dynamics in moving scenes. Researchers believe this is because AI neural networks were inspired by the infrastructure of the part of the brain that processes static images, which is different from the area of the brain that processes dynamic social scenes.
To build the experimental stations of the future, scientists at the National Synchrotron Light Source II (NSLS-II), a U.S. Department of Energy (DOE) Office of Science user facility at DOE's Brookhaven National Laboratory, are learning from some of the challenges that face them today. As light source technologies and capabilities continue to advance, researchers must navigate increasingly complex workflows and swiftly evolving experimental demands.
Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains error free.
Combining two different kinds of signals could help engineers build prosthetic limbs that better reproduce natural movements, according to a new study. A combination of electromyography and force myography is more accurate at predicting hand movements than either method by itself.
An international team has explored how in future aerial robots could process construction materials precisely in the air -- an approach with great potential for difficult-to-access locations or work at great heights. The flying robots are not intended to replace existing systems on the ground, but rather to complement them in a targeted manner for repairs or in disaster areas, for instance.
Helping music professionals explore the potential of generative AI
For a robot, the real world is a lot to take in. Making sense of every data point in a scene can take a huge amount of computational effort and time. Using that information to then decide how to best help a human is an even thornier exercise.
The core innovation of our new patent lies in its self-supervised approach to depth estimation. What that means is, the AI learns to judge the depth of an item it needs to pick up by using its own sensor data and the feedback it gets from its actions.
New research led by Imperial College London and co-authored by the University of Bristol, has revealed that aerial robotics could provide wide-ranging benefits to the safety, sustainability and scale of construction.
Inspired by the movements of a tiny parasitic worm, engineers have created a 5-inch soft robot that can jump as high as a basketball hoop. Their device, a silicone rod with a carbon-fiber spine, can leap 10 feet high even though it doesn't have legs. The researchers made it after watching high-speed video of nematodes pinching themselves into odd shapes to fling themselves forward and backward.
Inspired by the movements of a tiny parasitic worm, Georgia Tech engineers have created a 5-inch soft robot that can jump as high as a basketball hoop.
After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists combine elements of different methods to improve algorithms or create new ones.