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Researchers have developed a new type of electronic textile that could significantly enhance how robots interact with the world, giving them a level of tactile feedback much closer to that of human hands. The flexible sensor system, described in a study published on July 30 in Nature Communications, detects both pressure and slipping motions—capabilities that have long been difficult to replicate in robotic systems.
While robots can perform many tasks with speed and consistency, fine motor control remains a challenge. Machines often lack the nuanced sense of touch needed to grip objects gently without dropping or crushing them. The new textile sensor, inspired by human skin, aims to change that.
The sensor was integrated into 3D-printed robotic fingers attached to a compliant gripper. In tests, the system could detect minute shifts in pressure and object movement. According to TechXplore, when a copper weight was pulled from the gripper, the fingers responded in real time by tightening their hold—similar to how a person would react.
At the core of the sensor’s function is the tribovoltaic effect. When two surfaces in the sensor experience slippage, friction generates a small direct-current electrical signal. This signal provides immediate feedback to the system, triggering adjustments in grip strength or position.
The response time ranged between 0.76 and 38 milliseconds, closely matching the reaction time of human touch receptors, which typically respond within 1 to 50 milliseconds. This speed makes the sensor suitable for applications requiring precise timing and control.
Beyond robotic grasping, the technology could support various use cases, including prosthetic devices, minimally invasive surgical tools, and human-machine interfaces in manufacturing. The team is also exploring the integration of reinforcement learning, a form of AI, to improve the robot’s adaptability and dexterity in unfamiliar tasks.
As robots become more common in collaborative environments, solutions like this e-textile sensor offer a promising path toward safer, more intuitive interaction between machines and people.
