Humanoid robots are supposed to be our loyal assistants, but we saw another side to them the other day. Chinese robot manufacturer Unitree was demonstrating its latest H1 robots at a lantern festival in the city of Taishan, Guangdong province, when one walked up to the crowd barrier and seemed to lunge at an elderly woman, nearly headbutting her.
Over the past few years, engineers have developed increasingly advanced robotic systems already introduced in some public spaces and could soon be deployed in home environments. Many of these robots are humanoids, meaning that their body structure and physical features resemble those of humans.
From mountain goats that run up near-vertical rock faces to armadillos that roll into a protective ball, animals have evolved to adapt effortlessly to changes in their environment. In contrast, when an autonomous robot is programmed to reach a goal, each variation in its pre-determined path presents a significant physical and computational challenge.
Springtails, small bugs often found crawling through leaf litter and garden soil, are expert jumpers. Inspired by these hopping hexapods, roboticists at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have made a walking, jumping robot that pushes the boundaries of what small robots can do.
Many industrial processes and household tasks currently completed by humans entail the manipulation of textiles, including clothes, sheets, towels, cloths and other fabric-based objects. Most robotic systems developed so far do not reliably manipulate all types of textiles, due to challenges associated with predicting how these objects will deform when grasped and handled.
Indoor search and rescue operations are some of the most dangerous tasks that law enforcement and first responders must face, but drone technology has revolutionized how they approach these intense situations, according to graduate students in Penn State's Autonomous Robotics Competition Club (ARCC). Drones can be used to locate people in claustrophobic, dark and GPS-limited environments like collapsed buildings, but developing these drones is difficult and expensive.
An autonomous underwater vehicle can propel itself efficiently by using the energy in nearby water currents.
By watching their own motions with a camera, robots can teach themselves about the structure of their own bodies and how they move, a new study by researchers at Columbia Engineering now reveals. Equipped with this knowledge, the robots could not only plan their own actions, but also overcome damage to their bodies.
Humanoid robots, which have a body structure that mirrors that of humans, could rapidly and effectively tackle a wide range of tasks in real-world settings. These robots and their underlying control algorithms have improved considerably in recent years. Many of them can now move faster, emulating various human-like movements.
Researchers from the Shenyang Institute of Automation of the Chinese Academy of Sciences have developed a multi-mode swimming soft robotic fish. Drawing inspiration from the highly sensitive lateral line sensing system and advanced muscle actuation mechanisms of natural fish, the new design integrates actuation, perception, and control capabilities, offering significant advancements in underwater robotics.
Researchers have engineered groups of robots that behave as smart materials with tunable shape and strength, mimicking living systems. "We've figured out a way for robots to behave more like a material," said Matthew Devlin, a former doctoral researcher in the lab of University of California, Santa Barbara (USCB) mechanical engineering professor Elliot Hawkes, and the lead author of the article published in the journal Science.
In recent years, roboticists and computer scientists have developed a wide range of systems inspired by nature, particularly by humans and animals. By reproducing animal movements and behaviors, these robots could navigate real-world environments more effectively.
Humans are known to accumulate knowledge over time, which in turn allows them to continuously improve their abilities and skills. This capability, known as lifelong learning, has so far proved difficult to replicate in artificial intelligence (AI) and robotics systems.
Swimming robots play a crucial role in mapping pollution, studying aquatic ecosystems, and monitoring water quality in sensitive areas such as coral reefs or lake shores. However, many devices rely on noisy propellers, which can disturb or harm wildlife. The natural clutter in these environments—including plants, animals, and debris—also poses a challenge to robotic swimmers.
A recent breakthrough in photothermal actuator design has been achieved by a research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, led by Prof. Tian Xingyou and Prof. Zhang Xian. The team developed a novel superstructure liquid metal/low expansion polyimide/polydimethylsiloxane (LM@PI/PDMS) actuator, which combines rapid movement with impressive load-carrying capacity—an achievement that has eluded previous actuator designs.