Humans can grab a book from a shelf with little obvious thought. But it's a complex process for the brain that involves planning and navigating around obstacles, like other books or knickknacks. Robotics researchers have struggled to replicate this kind of human movement when their systems perform similar tasks. Known as motion planning, the process of training a robot to get an object from one point to another without hitting any obstacles takes time and resources because the robot can't react dynamically like humans in unknown environments.
Robots are widely used in the automotive industry and have started entering new application domains such as logistics in the last few years. However, current robots still face many limitations. They typically perform a single action or a fixed sequence of actions, repeating them the same way each time.
The emerging low-altitude economy brings unconventional opportunities for urban and economic development, including revolutionizing parcel delivery. Researchers at the Hong Kong Polytechnic University (PolyU) have utilized cutting-edge technology to pioneer the extensive application of unmanned drones in different aspects of life.
Drones can make air freight cheaper and remote areas more connected. But tracking them will be key.
Drones can make air freight cheaper and remote areas more connected. But tracking them will be key.
Engineers working on Google's DeepMind project have announced the development of two new AI-based robot systems. One called ALOHA Unleashed was developed to advance the science of bi-arm manipulation. The other, called DemoStart, was developed to advance the capabilities of robot hands that have multiple fingers, joints, or sensors.
While roboticists have introduced increasingly sophisticated robotic systems over the past decades, most of the solutions introduced so far are pre-programmed and trained to tackle specific tasks. The ability to continuously teach robots new skills while interacting with them could be highly beneficial and could facilitate their widespread use.
A set of knee exoskeletons, built with commercially available knee braces and drone motors at the University of Michigan, has been shown to help counteract fatigue in lifting and carrying tasks. They helped users maintain a better lifting posture even when tired, a key factor in defending against on-the-job injuries, say the researchers of a new paper published in the journal Science Robotics.
Scientists at the Max-Planck-Institute for Intelligent Systems (MPI-IS) have developed hexagon-shaped robotic components, called modules, that can be snapped together LEGO-style into high-speed robots that can be rearranged for different capabilities.
Ensuring that robots can smoothly collaborate with humans in real-world environments is a crucial step towards their large-scale deployment. While some robotic systems are already engaging daily with human agents, for instance at partially automated industrial and manufacturing facilities, human-robot collaboration on everyday tasks remains scarce.
Many animal species, ranging from insects to amphibians and fish, use jumping as a means of moving within their surrounding environment. Jumping can be very advantageous for these animals, for instance, allowing them to reach higher branches of trees, swiftly escape from predators or move faster across long distances.
Research at Michigan State University is focused on teaching robots to use colors to perceive, visualize, and interpret interactions when manipulating objects. A force-interpreting optical system is being developed so robots can distinguish and manipulate soft and fragile objects—which will be particularly helpful for medical and other assistive robots.
From search-and-rescue missions to orthopedic therapy and many other applications, soft robots and wearable electronic devices show great promise for many fields. However, designing them to be functional and practical to use has proved challenging.
A team of roboticists at the German Aerospace Center's Institute of Robotics and Mechatronics finds that combining traditional internal force-torque sensors with machine-learning algorithms can give robots a new way to sense touch.
Cornell University researchers have created microscale robots less than 1 millimeter in size that are printed as a 2D hexagonal "metasheet," but with a jolt of electricity, morph into preprogrammed 3D shapes and crawl.