Robots that can navigate various terrains both rapidly and efficiently could be highly advantageous, as they could successfully complete complex missions in challenging environments. For instance, these robots could help to monitor complex natural environments, such as forests, or could search for survivors after natural disasters.
To successfully complete missions in dynamic and unstructured real-world environments, mobile robots should be able to adapt their actions in real-time to avoid collisions with nearby objects, people or animals.
Engineers at The University of Manchester have unlocked the secrets to designing a robot capable of jumping 120 meters—higher than any other jumping robot designed to date.
Eindhoven researchers have developed a soft robotic "hand" made from liquid crystals and graphene that could be used to design future surgical robots. The new work has just been published in the journal ACS Applied Materials & Interfaces.
Dr. Peter King, a manufacturing researcher at our Clayton site, came across a problem in the lab six years ago. He found that before the team could start any lab work, they had to spend a lot of time programming.
New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation.
While roboticists have introduced increasingly sophisticated systems over the past decades, teaching these systems to successfully and reliably tackle new tasks has often proved challenging. Part of this training entails mapping high-dimensional data, such as images collected by on-board RGB cameras, to goal-oriented robotic actions.
The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts often collected from the internet.
A fully edible robot could soon end up on our plate if we overcome some technical hurdles, say EPFL scientists involved in RoboFood—a project which aims to marry robots and food.
A team of roboticists at the University of Tokyo has taken a new approach to autonomous driving—instead of automating the entire car, simply put a robot in the driver's seat. The group built a robot capable of driving a car and tested it on a real-world track. They also published a paper describing their efforts on the arXiv preprint server.
Artificial intelligence (AI) systems that can play games with humans have become increasingly advanced and have already been deployed by countless videogame developers worldwide. Most of these systems, however, are designed to compete against humans online, on digital platforms and in virtual environments, as opposed to physically in the real-world.
A robotic gripper developed by Washington State University researchers is able to gently grab the majority of apples out of a tree without damaging the fruit.
Someday, you may want your home robot to carry a load of dirty clothes downstairs and deposit them in the washing machine in the far-left corner of the basement. The robot will need to combine your instructions with its visual observations to determine the steps it should take to complete this task.
Robotic systems are already being deployed in various settings worldwide, assisting humans with a highly diverse range of tasks. One sector in which robots could prove particularly advantageous is agriculture, where they could complete demanding manual tasks faster and more efficiently.
Researchers from North Carolina State University have demonstrated miniature soft hydraulic actuators that can be used to control the deformation and motion of soft robots that are less than a millimeter thick. The researchers have also demonstrated that this technique works with shape memory materials, allowing users to repeatedly lock the soft robots into a desired shape and return to the original shape as needed.