Researchers at Northwestern University and Israel's Tel Aviv University have overcome a major barrier to achieving a low-cost solution for advanced robotic touch. The authors argue that the problem that has been lurking in the margins of many papers about touch sensors lies in the robotic skin itself.
Digital twins are a rapidly advancing area in engineering, going beyond static models to continuously receive data from the physical world and make predictions that go on to affect that reality. They have applications in areas such as energy systems, manufacturing and medicine. U-M's Automotive Research Center (ARC) uses them to help design, test and control autonomous off-road vehicles that operate in human-led teams.
Delivery robots made by companies such as Starship Technologies and Kiwibot autonomously make their way along city streets and through neighborhoods.
Recent advances in robotics and machine learning have enabled the automation of many real-world tasks, including various manufacturing and industrial processes. Among other applications, robotic and artificial intelligence (AI) systems have been successfully used to automate some steps in manufacturing clothes.
Many robotic applications rely on robotic arms or hands to handle different types of objects. Estimating the pose of such hand-held objects is an important yet challenging task in robotics, computer vision and even in augmented reality (AR) applications. A promising direction is to utilize multi-modal data, such as color (RGB) and depth (D) images. With the increasing availability of 3D sensors, many machine learning approaches have emerged to leverage this technique.
Trees, vegetation, rocks, unpredictable terrain and the lack of clearly defined roads—or roads at all—won't stop an autonomous, off-road vehicle developed by researchers at Carnegie Mellon University's Robotics Institute.
Fish are masters of coordinated motion. Schools of fish have no leader, yet individuals manage to stay in formation, avoid collisions, and respond with liquid flexibility to changes in their environment. Reproducing this combination of robustness and flexibility has been a long-standing challenge for human-engineered systems like robots.
For delivery robots, not all sidewalks are created equal—some are uneven or clogged with people and bus shelters—so researchers at Cornell Tech developed a "robotability score" and rated every street in New York City on how hospitable it would be to robots.
Unmanned aerial vehicles (UAVs), commonly known as drones, have already proved to be valuable tools for a wide range of applications, ranging from film and entertainment production to defense and security, agriculture, logistics, construction and environmental monitoring. While these technologies are already widely used in many countries worldwide, engineers have been trying to enhance their capabilities further so that they can be used to tackle even more complex problems.
Tracking thousands of products in massive warehouses presents a logistical nightmare for many businesses. To address this, Kennesaw State University assistant professor Jian Zhang has introduced an autonomous robot that can log inventory.
At a time when we run ourselves ragged to meet society's expectations of productivity, performance and time optimization, is it right that our robot vacuum cleaners and other smart appliances should sit idle for most of the day?
Researchers have created a light-powered soft robot that can carry loads through the air along established tracks, similar to cable cars or aerial trams. The soft robot operates autonomously, can climb slopes at angles of up to 80 degrees, and can carry loads up to 12 times its weight.
A team of roboticists at Tsinghua University, working with a trio of colleagues from Beihang University, all in China, has designed a new type of microrobot that can continuously transform its shape and "lock" into specific configurations. In their paper published in the journal Nature Machine Intelligence, the group describes the factors that went into their design, the capabilities of the microrobots and possible uses for them.
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.
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.