Researchers at the National University of Singapore have developed a soft robot system that gives machines a human-like sense of body awareness, allowing them to detect touch, external forces, and movement without relying on cameras or external tracking systems.

The research focuses on proprioception, often called the body’s “sixth sense,” which helps humans understand body position and movement without looking. The team recreated a similar capability in soft robots using what they call an “expected perception” framework.

The system allows the robot to predict how its body should move and compare that prediction with real-time sensory feedback. Any mismatch signals external contact or environmental interaction. Researchers say this helps the robot distinguish between its own movement and outside forces, something that has long challenged soft robotics.

To test the technology, the team equipped a flexible robot with liquid-metal-based sensors capable of measuring bending, strain, and deformation. The robot then used internal sensing to navigate and react to physical interactions in real time.

Robots gain sixth sense

“Soft robots, too, need proprioception,” said Professor Cecilia Laschi from the Department of Mechanical Engineering at the National University of Singapore.

According to the researchers, traditional soft robots struggle because their strain sensors react both to their own movement and to outside contact, making it difficult to determine what is actually happening around them.

The new framework addresses that issue by mimicking how the human brain predicts sensory feedback. The robot calculates its expected body position based on movement commands and compares it with sensor readings gathered from its flexible structure.

Researchers tested the system in a maze-navigation experiment where the robot moved autonomously without cameras. Instead, it relied entirely on touch and internal sensing to detect walls and adjust its movement path.

In another test, a human operator guided the robot through movements similar to a massage or medical procedure performed on a manikin. The robot then learned and repeated those movements with high accuracy.

Camera-free navigation emerges

“It could detect external contact within 0.4 seconds and distinguish its source with remarkable precision,” said Prof Laschi. “The robot also identified the direction of applied forces with an error margin below 10 degrees, even in dynamic environments.”

The researchers believe the technology could improve human-robot interaction in healthcare, rehabilitation, and assistive robotics. Soft robots equipped with advanced sensing could eventually help elderly users, assist caregivers, or support surgeons during minimally invasive procedures.

The team also sees applications in underwater robotics. Robots inspired by octopus arms, for example, could use touch-based perception to navigate environments where cameras may struggle due to darkness or poor visibility.

“Robotics is inherently a cross-disciplinary field,” added Prof Laschi, pointing to the growing role of neuroscience, material science, artificial intelligence, and biology in shaping future robotic systems.

Going forward, the researchers plan to improve the prediction system using machine learning models inspired by how human brains build internal representations from experience.

The findings were published in Nature Communications.