HMN 2025: How Innovative robotic slip-prevention technique might deliver human-like dexterity to industrial automation

A brand new slip-prevention technique has been proven to enhance how robots grip and deal with fragile, slippery or uneven objects, in accordance with a University of Surrey–led study published in Nature Machine Intelligence. The innovation might pave the way in which for safer, extra dependable automation throughout industries starting from manufacturing to well being care.

In the review, researchers from Surrey’s School of Computer Science and Electronic Engineering demonstrated how their method permits robots to foretell when an object may slip—and adapt their actions in real-time to stop it.

Similar to the way in which people naturally regulate their motions, this bio-inspired technique outperforms conventional grip-force methods by permitting robots to maneuver extra intelligently and keep a safe maintain with out merely squeezing tougher.

“If you think about carrying a plate that begins to slide, most individuals do not merely squeeze tougher—they instinctively regulate their hand’s movement by slowing down, tilting or repositioning to cease it from falling. Traditionally, robots have been educated to rely solely on grip pressure, which may be ineffective and even damaging to delicate gadgets.

“We’ve taught our robots to take a extra human-like method, sensing when one thing may slip and robotically adjusting their actions to maintain objects safe.

“This could possibly be a recreation changer for future automation, from dealing with surgical instruments in well being care and assembling delicate components in manufacturing to sorting awkward packages in logistics or aiding individuals of their properties,” says Dr. Amir Esfahani, affiliate professor in robotics.

Working in collaboration with the University of Lincoln, Arizona State University, Korea Advanced Institute of Science and Technology (KAIST), and Toshiba Europe’s Cambridge Research Laboratory, the review is the primary to display and quantify the effectiveness of trajectory modulation for slip prevention in each people and robots.

The findings present {that a} predictive management system powered by a realized “tactile ahead model” permits robots to anticipate when an object is more likely to slip, constantly analyzing its deliberate actions.

Researchers additionally demonstrated that the system works on objects and motion paths it wasn’t educated on, highlighting its potential to generalize successfully to real-world settings.

“We consider that our method has notable potential in quite a lot of industrial and repair robotic purposes, and our work opens up new alternatives to deliver robots into our day by day life. We hope our findings will encourage future analysis on this space and additional advance the sector of robotics,” says Esfahani.