While space structures and robotic arms require lightweight actuation devices capable of repetitive movement, conventional motor-based systems face limitations due to their heavy weight and complex structures. A KAIST research team has developed a smart material-based actuation technology that operates rapidly in less than a second without a motor, suggesting new possibilities for next-generation robotics and space deployable structures.While space structures and robotic arms require lightweight actuation devices capable of repetitive movement, conventional motor-based systems face limitations due to their heavy weight and complex structures. A KAIST research team has developed a smart material-based actuation technology that operates rapidly in less than a second without a motor, suggesting new possibilities for next-generation robotics and space deployable structures.[#item_full_content]

A bird banking in a crosswind doesn’t rely on spinning blades. Its wings flex, twist and respond instantly to its environment. Engineers at Rutgers University have taken a major step toward building bird-like drones that move the same way, flapping their wings like real birds, using electricity-driven materials instead of conventional electromagnetic motors to power them.A bird banking in a crosswind doesn’t rely on spinning blades. Its wings flex, twist and respond instantly to its environment. Engineers at Rutgers University have taken a major step toward building bird-like drones that move the same way, flapping their wings like real birds, using electricity-driven materials instead of conventional electromagnetic motors to power them.[#item_full_content]

A collaborative research group has developed a bio-inspired robotic system based on insect behavior which can locate odor sources both indoors and outdoors with consistent accuracy, even if one of its two sensors fails. The team includes Assistant Professor Shigaki Shunsuke of the National Institute of Informatics (NII), Professor Kurabayashi Daisuke of the School of Engineering at Science Tokyo, and Associate Professor Owaki Dai of the Graduate School of Engineering at Tohoku University.A collaborative research group has developed a bio-inspired robotic system based on insect behavior which can locate odor sources both indoors and outdoors with consistent accuracy, even if one of its two sensors fails. The team includes Assistant Professor Shigaki Shunsuke of the National Institute of Informatics (NII), Professor Kurabayashi Daisuke of the School of Engineering at Science Tokyo, and Associate Professor Owaki Dai of the Graduate School of Engineering at Tohoku University.[#item_full_content]

Scientists have developed a network of mechanical motors that mimic the molecular machinery underpinning human muscle contraction. The University of Bristol-led findings, published in the Journal of the Royal Society Interface this week, could open new possibilities for artificial muscles in robotics.Scientists have developed a network of mechanical motors that mimic the molecular machinery underpinning human muscle contraction. The University of Bristol-led findings, published in the Journal of the Royal Society Interface this week, could open new possibilities for artificial muscles in robotics.[#item_full_content]

Robots are increasingly learning new skills by watching people. From folding laundry to handling food, many real-world, humanlike tasks are too nuanced to be efficiently programmed step by step.Robots are increasingly learning new skills by watching people. From folding laundry to handling food, many real-world, humanlike tasks are too nuanced to be efficiently programmed step by step.[#item_full_content]

Roboticists have struggled to get humanoid robots to effectively replicate athletic sports skills, such as those needed for tennis. These sports require highly dynamic motion, quick reactions, and high precision that robots are not usually equipped to handle. Past research attempted to use kinematic data and video-based extraction of human motion data, but these approaches were complex and often physically infeasible. Some robots have been trained to play sports like table tennis or football, but with limited agility and realism.Roboticists have struggled to get humanoid robots to effectively replicate athletic sports skills, such as those needed for tennis. These sports require highly dynamic motion, quick reactions, and high precision that robots are not usually equipped to handle. Past research attempted to use kinematic data and video-based extraction of human motion data, but these approaches were complex and often physically infeasible. Some robots have been trained to play sports like table tennis or football, but with limited agility and realism.[#item_full_content]

NUS researchers have developed a platform that lets lab-grown muscle tissues train themselves to record-breaking strength, with no external stimulation required. By mechanically coupling two muscle tissues so they continuously pull against each other, their own natural contractions become a round-the-clock workout. The resulting muscles powered OstraBot, an ostraciiform (a type of fish locomotion) swimming robot that reached 467 millimeters per minute—the fastest speed reported for any skeletal muscle-driven biohybrid robot.NUS researchers have developed a platform that lets lab-grown muscle tissues train themselves to record-breaking strength, with no external stimulation required. By mechanically coupling two muscle tissues so they continuously pull against each other, their own natural contractions become a round-the-clock workout. The resulting muscles powered OstraBot, an ostraciiform (a type of fish locomotion) swimming robot that reached 467 millimeters per minute—the fastest speed reported for any skeletal muscle-driven biohybrid robot.[#item_full_content]

MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches.MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches.[#item_full_content]

Sheepdogs, bred to control large groups of sheep in open fields, have demonstrated their skills in competitions dating back to the 1870s. In these contests, a handler directs a trained dog with whistle signals to guide a small group of sheep across a field and sometimes split the flock cleanly into two groups. But sheep do not always cooperate.Sheepdogs, bred to control large groups of sheep in open fields, have demonstrated their skills in competitions dating back to the 1870s. In these contests, a handler directs a trained dog with whistle signals to guide a small group of sheep across a field and sometimes split the flock cleanly into two groups. But sheep do not always cooperate.[#item_full_content]