The researchers, from the University of Cambridge, studied industry data from the UK and 24 other European countries between 1995 and 2017, and found that at low levels of adoption, robots have a negative effect on profit margins. But at higher levels of adoption, robots can help increase profits. According to the researchers, this U-shaped phenomenon is due to the relationship between reducing costs, developing new processes and innovating new products. While many companies first adopt robotic technologies to decrease costs, this'process innovation' can be easily copied by competitors, so at low levels of robot adoption, companies are focused on their competitors rather than on developing new products. However, as levels of adoption increase and robots are fully integrated into a company's processes, the technologies can be used to increase revenue by innovating new products.

However, the rigid computer chips traditionally needed to enable advanced robot capabilities -- sensing, analyzing and responding to the environment -- add extra weight to the thin sheet materials and makes them harder to fold. The semiconductor-based components therefore have to be added after a robot has taken its final shape. Now, a multidisciplinary team led by researchers at the UCLA Samueli School of Engineering has created a new fabrication technique for fully foldable robots that can perform a variety of complex tasks without relying on semiconductors. A study detailing the research findings was published in Nature Communications. By embedding flexible and electrically conductive materials into a pre-cut, thin polyester film sheet, the researchers created a system of information-processing units, or transistors, which can be integrated with sensors and actuators.

"The hang up is what's going on in the robot's head," she adds. Perceiving stimuli and calculating a response takes a "boatload of computation," which limits reaction time, says Neuman, who recently graduated with a PhD from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Neuman has found a way to fight this mismatch between a robot's "mind" and body. The method, called robomorphic computing, uses a robot's physical layout and intended applications to generate a customized computer chip that minimizes the robot's response time. The advance could fuel a variety of robotics applications, including, potentially, frontline medical care of contagious patients.

To navigate built environments, robots must be able to sense and make decisions about how to interact with their locale. Researchers at the company were interested in using machine and deep learning to train their robots to learn about objects, but doing so requires a large dataset of images. While there are millions of photos and videos of rooms, none were shot from the vantage point of a robotic vacuum. Efforts to train using images with human-centric perspectives failed. Beksi's research focuses on robotics, computer vision, and cyber-physical systems.

Artificial neural networks, layers of interconnected artificial neurons, are of great interest for machine learning tasks such as speech recognition and medical diagnosis. Actually, electronic computing hardware are nearing the limit of their capabilities, yet the demand for greater computing power is constantly growing. Researchers turned themselves to photons instead of electrons to carry information at the speed of light. In fact, not only photons can process information much faster than electrons, but they are the basis of the current Internet, where it is important to avoid the so-called electronic bottleneck (conversion of an optical signal into an electronic signal, and vice versa). The proposed optical neural network is capable of recognizing and processing large-scale data and images at ultra-high computing speeds, beyond ten trillion operations per second.

The very fact that objects in your sight may obscure your view of things that lie further ahead is blindingly obvious to people. But Peiyun Hu, a Ph.D. student in CMU's Robotics Institute, said that's not how self-driving cars typically reason about objects around them. Rather, they use 3D data from lidar to represent objects as a point cloud and then try to match those point clouds to a library of 3D representations of objects. The problem, Hu said, is that the 3D data from the vehicle's lidar isn't really 3D -- the sensor can't see the occluded parts of an object, and current algorithms don't reason about such occlusions. "Perception systems need to know their unknowns," Hu observed.