Built from flexible, compliant materials, soft robots are gaining relevance for tasks ranging from minimally invasive surgery to deep-sea exploration but remain held back by a fundamental constraint. To sense their surroundings and react, most soft robots rely on separate electronic sensors, signal-processing circuits and powered actuators, all coordinated by computers. This chain of components adds weight, complexity and points of failure, particularly in wet, hot or high-pressure settings where electronics are highly susceptible to disruption.

A research team at the College of Design and Engineering, National University of Singapore (NUS CDE), has developed a mechanical soft force sensor that eliminates this electronic chain entirely. The sensor converts an applied force directly into fluid flow that drives actuators, creating a complete sensing-to-action loop with no computation or external energy. The approach could lead to soft robots that are simpler to build and capable of operating in environments that compromise conventional electronics.

A high-performance biodegradable pressure sensor has been developed as a key technology for wearable AI healthcare robotics and user-friendly robotic systems.

Seoul National University College of Engineering announced that a research team led by Professor Tae-Woo Lee in the Department of Materials Science and Engineering has developed an eco-friendly artificial spiderweb pressure sensor* inspired by the multiscale architecture of natural spiderwebs. The sensor simultaneously achieves high sensitivity, fast response time, and excellent mechanical stability.

* Artificial spiderweb pressure sensor: A flexible pressure sensor that mimics the multilayered fiber and mesh structures of spiderwebs to efficiently transmit pressure and convert it into electrical signals.

Capable of real-time detection of human pulse, respiration, vocalization, and finger movements, the sensor can be integrated with robotic hand control systems and is expected to find broad applications in Parkinson’s disease rehabilitation assistive devices, eco-friendly wearable devices, and user-friendly robotics.

The research was published on July 4 in Nature Communications, a world-renowned international academic journal.

Researchers have proposed a new rehabilitation design that uses brain signals not to control movement directly, but to decide when and how strongly simple haptic cues should be delivered. Combined with wearable devices based on McKibben artificial muscles, this approach may provide intuitive, low-burden guidance that supports motor learning while reducing overreliance on visual and verbal instructions.

Non line of sight (NLOS) imaging achieves imaging of occluded objects by analyzing the reflection and scattering of relay surfaces. Deep learning methods are mainly used to restore two-dimensional hidden scenes and face challenges in reconstructing complex real-world scenes. To solve the problems, a research team led by Hao Liu and Mingliang Xu published their new study in Frontiers of Computer Science on 15 May 2026.