Our newsfeeds are filled with talk about the rapid rise of artificial intelligence (AI) in software such as ChatGPT and Stable Diffusion, which can quickly—albeit haphazardly—generate works such as essays and photographs from a text prompt. Reading these, you might be excused for thinking that writers and photographers are soon to go the way of the elevator operator, automated out of existence.
Learning from one's past mistakes is not limited to humans. Computers do it, too. In industries, this is done via computer-based control systems that help operate production systems. For industrial robots that perform specific tasks in batches, say producing clothing, computer chips, or baked goods, the most commonly used control technique is iterative learning control (ILC). Most industries still rely on ILC systems that use a learning strategy called the proportional-type update rule (PTUR). This technique improves the performance of ILC systems by repeating the same task over and over and updating its control input based on errors encountered in previous iterations.
Many existing robotics systems draw inspiration from nature, artificially reproducing biological processes, natural structures or animal behaviors to achieve specific goals. This is because animals and plants are innately equipped with abilities that help them to survive in their respective environments, and that could thus also improve the performance of robots outside of laboratory settings.
Researchers have trained a robotic 'chef' to watch and learn from cooking videos, and recreate the dish itself.
Stanford scientists have developed a soft and stretchable electronic skin that can directly talk to the brain, imitating the sensory feedback of real skin using a strategy that, if improved, could offer hope to millions of people with prosthetic limbs.
The accelerated pace of robotics development has given us a veritable zoo filled with creatures sometimes indistinguishable from the real deal.
Divers are often put at considerable risk when searching for people or objects underwater. The ETH spin-off Tethys has developed an underwater robot that can be used in situations that are too dangerous for human divers.
Ameca can speak French, Chinese or dozens of other languages, instantly compose a poem or sketch a cat on request. Ask for a smile, and you'll get a clenched grin on her rubbery blue face.
When a kitten is walking in a dangerous environment, it will gently step on the terrain with its feet to estimate the friction or bearing capacity. Based on this experience, the kitten can then predict the physical parameters of terrain with a similar appearance and avoid the soft, wet ground.
Imagine a world where science fiction meets reality, where cutting-edge technology brings to life the awe-inspiring scenes from movies like Prometheus. This is the groundbreaking research led by Dr. Fu Zhang, Assistant Professor of Department of Mechanical Engineering at the Faculty of Engineering, the University of Hong Kong (HKU), who has developed a Powered-flying Ultra-underactuated LiDAR-Sensing Aerial Robot (PULSAR) that is poised to redefine the world of unpiloted aerial vehicles (UAVs).
Researchers from the Department of Mechanical Science and Bioengineering at Osaka University have invented a new kind of walking robot that takes advantage of dynamic instability to navigate. By changing the flexibility of the couplings, the robot can be made to turn without the need for complex computational control systems. This work may assist the creation of rescue robots that are able to traverse uneven terrain.
Bio-inspired robots, robotic systems that emulate the appearance, movements, and/or functions of specific biological systems, could help to tackle real-world problems more efficiently and reliably. Over the past two decades, roboticists have introduced a growing number of these robots, some of which draw inspiration from fruit flies, worms, and other small organisms.
Neural radiance fields (NeRFs) are advanced machine learning techniques that can generate three-dimensional (3D) representations of objects or environments from two-dimensional (2D) images. As these techniques can model complex real-world environments realistically and in detail, they could greatly support robotics research.
The qualities that make a knitted sweater comfortable and easy to wear are the same things that might allow robots to better interact with humans.
Philosophers and legal scholars have explored significant aspects of the moral and legal status of robots, with some advocating for giving robots rights. As robots assume more roles in the world, a new analysis reviewed research on robot rights, concluding that granting rights to robots is a bad idea. Instead, the article looks to Confucianism to offer an alternative.