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Category: robotics/AI – Page 74

Going places: Muscle-inspired mechanism powers tiny autonomous insect robots

Science frequently draws inspiration from the natural world. After all, nature has had billions of years to perfect its systems and processes. Taking their cue from mollusk catch muscles, researchers have developed a low-voltage, muscle-like actuator that can help insect-scale soft robots to crawl, swim and jump autonomously in real-world settings. Their work solves a long-standing challenge in soft robotics: enabling tiny robots to move on their own without sacrificing power or precision.

Muscles are that work by contracting and relaxing to cause movement. Insect muscles are particularly good at this because they are incredibly powerful for their small size. Similarly, actuators are devices that convert mechanical energy into motion.

However, when it comes to robotics, creating tiny, powerful actuators that move with the same agility, precision and resilience as a biological has proved challenging. What’s more, the rigid motors in current robotic systems are difficult to scale down because they easily break.

Finding the shadows in a fusion system faster with AI

A public‑private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Oak Ridge National Laboratory has led to a new artificial intelligence (AI) approach that is faster at finding what’s known as “magnetic shadows” in a fusion vessel: safe havens protected from the intense heat of the plasma.

Known as HEAT‑ML, the new AI could lay the foundation for software that significantly speeds up the design of future systems. Such software could also enable good decision‑making during fusion operations by adjusting the plasma so that potential problems are thwarted before they start.

“This research shows that you can take an existing code and create an AI surrogate that will speed up your ability to get useful answers, and it opens up interesting avenues in terms of control and scenario planning,” said Michael Churchill, co‑author of a paper in Fusion Engineering and Design about HEAT‑ML and head of digital engineering at PPPL.

“They’re Building Data Fortresses For AI”: EU Unveils $30 Billion Plan For Gigawatt Centers Housing 100,000 GPUs Each To Rival US And China

IN A NUTSHELL 💡 The European Union plans to invest $30 billion to establish a network of high-capacity AI data centers. 🌍 This initiative aims to enhance the EU’s global standing in the artificial intelligence market. ⚙️ The project involves the development of gigawatt-scale data centers to support millions of AI GPUs. 🔌 Challenges include

Brain cells learn faster than machine learning, research reveals

Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as “DishBrain” and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.

The study, “Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning,” published in Cyborg and Bionic Systems, is the first known of its kind.

The research was led by Cortical Labs, the Melbourne-based startup which created the world’s first commercial biological computer, the CL1. The CL1, through which the research was conducted, fuses lab-cultivated neurons from human stem cells with hard silicon to create a more advanced and sustainable form of AI, known as SBI.

AI-Engineered Hydrogels Achieve Instant and Powerful Underwater Adhesion

Underwater adhesives have long posed a challenge to materials scientists, with few solutions capable of delivering instant, strong, and repeatable adhesion in challenging marine and biomedical environments. Now, a team of researchers has leveraged machine learning (ML) and data mining (DM) —inspired by natural adhesive proteins—to engineer next-generation super-adhesive hydrogels that work instantly underwater.

Published in Nature, the study introduces an end-to-end data-driven framework that starts with protein sequence extraction and ends with a scalable hydrogel synthesis method. The results are materials that can seal high-pressure leaks, attach securely to rough, wet surfaces, and even function in living tissue.

Robots learn human-like movement adjustments to prevent object slipping

To effectively tackle a variety of real-world tasks, robots should be able to reliably grasp objects of different shapes, textures and sizes, without dropping them in undesired locations. Conventional approaches to enhancing the ability of robots to grasp objects work by tightening the grip of a robotic hand to prevent objects from slipping.

Researchers at the University of Lincoln, Toshiba Europe’s Cambridge Research Laboratory, the University of Surrey, Arizona State University and KAIST recently introduced alternative computational strategies for preventing the slip of objects grasped by a robotic hand, which works by modulating the trajectories that a robotic hand follows while performing manipulative movements. Their approach, consisting of a robotic controller and a new bio-inspired predictive trajectory modulation strategy, was presented in a paper published in Nature Machine Intelligence.

“The inspiration for this paper came from a very human experience,” Amir Ghalamzan, senior author of the paper, told Tech Xplore.

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