Researchers create a soft electrohydraulic robot that adapts to terrestrial and aquatic environments

Imagine a robot that is crawling and exploring on land, as it encounters a body of water, it switches into swimming mode to keep going freely- this is not a sci-fi scenario, but the real-life capabilities of a multi-modal amphibious soft robot newly developed by a team of researchers. The highlight of this robot is its ability to switch smoothly between terrestrial and aquatic environments without any structural modifications or reconfiguration. What is more impressive is its ability to operate in extreme temperature environments, from -20°C to even 70°C.

The team published their findings in Cyborg and Bionic Systems on June 9, 2025.

A research paper by scientists at The Chinese University of Hong Kong proposed a 3D radar-based control scheme that realizes the navigated locomotion of microswimmers in 3D space with multiple static and dynamic obstacles. The new research paper, published on Jun. 2 in the journal Cyborg and Bionic Systems, presented a 3D hierarchical radar with a motion sphere and a detection sphere is firstly developed. Using the radar-based avoidance approach, the desired motion direction for the microswimmer to avoid obstacles can be obtained, and the coarse-to-fine search is used to decrease the computational load of the algorithm. Three navigation modes of the microswimmer in 3D space with dynamic conditions are realized by the radar-based navigation strategy that combines the global path planning algorithm and the radar-based avoidance approach.

In order to elucidate the mechanisms underlying NET activation and its impact on MIRI, Hongru Zhang/Senlei Xu of Nanjing University of Chinese Medicine (NJUCM), along with Yanfei Mo of Pukou Hospital of Chinese Medicine, confirm that the generation of NETs by neutrophils exacerbates microvascular endothelial cell injury, thereby worsening MIRI.

This work shows that multi-DOF metamaterials are limited by their energy-driven deformation, restricting their mechanical behaviors. By using one-DOF mechanisms and inverse design, the team created lightweight, highly deformable, easy-to-make metamaterials with tunable, programmable, and anisotropic mechanical responses. This opens a new path toward smart, reconfigurable materials for advanced engineering and adaptive applications.

To address the above issues, Prof. Zhongwen Li's team at Ningbo Eye Institute, Wenzhou Medical University, introduced the Ocular Surface Pretrained Model (OSPM), a novel self-supervised learning-based domain-specific model to mitigate the scarcity of labeled data. It leverages 0.76 million unlabeled ocular surface images from 10 clinical centers across China to learn ocular surface feature representations, which can be easily adapted for classifying OSTs.