For robots to be successfully introduced in a wider range of real-world settings, they should be able to safely and reliably navigate rapidly changing environments. While roboticists and computer scientists have introduced a wide range of computational techniques for robot navigation over the past decades, many of them were found to perform poorly in environments that are dynamic, cluttered or characterized by narrow pathways.
A new review paper on the latest trends and advancements in intuitive Human-Robot Interaction (HRI) using bio-potential and bio-impedance has been published in the journal Nature Reviews Electrical Engineering. The joint research team was led by Professor Jung Kim of KAIST Department of Mechanical Engineering and Professor Min-kyu Je of the Department of Electrical and Electronic Engineering.
A new slip-prevention method has been shown to improve how robots grip and handle fragile, slippery or asymmetric objects, according to a University of Surrey–led study published in Nature Machine Intelligence. The innovation could pave the way for safer, more reliable automation across industries ranging from manufacturing to health care.
Whether strawberries, asparagus or apples: when it comes to harvesting, skilled workers are often in short supply. Many researchers are therefore working on harvesting robots that could provide welcome support to agricultural businesses in the future.
Nature has long served as inspiration for cutting-edge engineering—especially in the realm of underwater propulsion.
A research team from the Ningbo Institute of Materials Technology and Engineering (NIMTE) of the Chinese Academy of Sciences has developed a new method to enhance the efficiency of dynamics modeling for industrial robots, tackling long-standing bottlenecks in real-time torque computation.
While the advent of robotic systems that can complete household chores has been widely anticipated, those commercially released so far are primarily robot vacuums that autonomously clean the floor. In contrast, robots that can reliably clean surfaces, tidy up, cook or perform other tasks in home environments are either too expensive or have not yet reached the market.
Unmanned aerial vehicles (UAVs), commonly known as drones, are now widely used worldwide to tackle various real-world tasks, including filming videos for various purposes, monitoring crops or other environments from above, assessing disaster zones, and conducting military operations. Despite their widespread use, most existing drones either need to be fully or partly operated by human agents.
For search and rescue, AI is not more accurate than humans, but it is far faster.
Teaching a robot new skills used to require coding expertise. But a new generation of robots could potentially learn from just about anyone.
While roboticists have introduced increasingly advanced systems over the past decades, most existing robots are not yet able to manipulate objects with the same dexterity and sensing ability as humans. This, in turn, adversely impacts their performance in various real-world tasks, ranging from household chores to the clearing of rubble after natural disasters and the assembly or performing maintenance tasks, particularly in high-temperature working environments such as steel mills and foundries, where elevated temperatures can significantly degrade performance and compromise the precision required for safe operations.
A cheetah's powerful sprint, a snake's lithe slither, or a human's deft grasp: Each is made possible by the seamless interplay between soft and rigid tissues. Muscles, tendons, ligaments, and bones work together to provide the energy, precision, and range of motion needed to perform the complex movements seen throughout the animal kingdom.
Today's robots are stuck—their bodies are usually closed systems that can neither grow nor self-repair, nor adapt to their environment. Now, scientists at Columbia University have developed robots that can physically "grow," "heal," and improve themselves by integrating material from their environment or from other robots.
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial task. In this context, Korean researchers are gaining attention for newly implementing an artificial sensory nervous system that mimics the sensory nervous system of living organisms without the need for separate complex software or circuitry. This breakthrough technology is expected to be applied in fields such as in ultra-small robots and robotic prosthetics, where intelligent and energy-efficient responses to external stimuli are essential.
How do you develop an AI system that perfectly mimics the way humans speak? Researchers at Nagoya University in Japan have taken a significant step forward to achieve this. They have created J-Moshi, the first publicly available AI system specifically designed for Japanese conversational patterns.