Category Robotics Classification

Researchers are blurring the lines between robotics and materials, with a proof-of-concept material-like collective of robots with behaviors inspired by biology.

Groundbreaking study shows machine learning can decode emotions in seven ungulate species. A game-changer for animal welfare? Can artificial intelligence help us understand what animals feel? A pioneering study suggests the answer is yes. Researchers have successfully trained a machine-learning model to distinguish between positive and negative emotions in seven different ungulate species, including cows, pigs, and wild boars. By analyzing the acoustic patterns of their vocalizations, the model achieved an impressive accuracy of 89.49%, marking the first cross-species study to detect emotional valence using AI.

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.

This surge is fueled by automation in industries such as manufacturing, logistics, and warehousing. North America is projected to lead with a 15.4% CAGR, while Asia Pacific is set to grow at 18.3%, driven by rapid industrialization.

Claire chatted to Catherine Menon from the University of Hertfordshire about designing home assistance robots with ethics in mind.

Catherine Menon is a principal lecturer at the University of Hertfordshire. Her research explores the ethics and safety of autonomous systems, and she has a particular interest in the interaction between safety requirements, ethical imperatives and trust constraints in public-facing AI including assistive robots. She has previously worked as a safety-critical systems engineer in the defence and nuclear sectors, and has been involved in producing and validating several international standards for these domains.

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.

A novel system that chases larval zebrafish around an arena with predator robots is enabling scientists to understand how these days-old fish quickly learn in the real world.

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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.

Engineers have developed a versatile swimming robot that nimbly navigates cluttered water surfaces. Inspired by marine flatworms, the innovative device offers new possibilities for environmental monitoring and ecological research.

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.

Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs integrate data inputs across modalities in a central hub that processes data in an input-type-agnostic fashion.

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.

Ballbots are versatile robotic systems with the ability to move around in all directions. This makes it tricky to control their movement. In a recent study, a team has proposed a novel proportional integral derivative controller that, in combination with radial basis function neural network, robustly controls ballbot motion. This technology is expected to find applications in service robots, assistive robots, and delivery robots.

The ballbot is a unique kind of robot with great mobility and possesses the ability to go in all directions. Obviously, controlling such a robotic device must be tricky. Indeed, ballbot systems pose unique challenges, particularly in the form of the difficulty of maintaining balance and stability in dynamic and uncertain environments.