Robots can sense when something might slip from grip with new method

Engineers at the University of Surrey have developed a novel, bio-inspired method enabling robots to sense and prevent objects from slipping during manipulation by predicting slip events and adjusting their movements in real-time. Unlike traditional robotic grip strategies that rely solely on increasing grip force—which can damage delicate items—the new approach mimics human behavior by modulating the robot’s trajectory, such as slowing down or repositioning, to maintain a secure hold without excessive squeezing. This method, demonstrated through a predictive control system powered by a learned tactile forward model, allows robots to anticipate slip risks continuously and adapt accordingly. The research, published in Nature Machine Intelligence, shows that trajectory modulation significantly outperforms conventional grip-force-based slip control in certain scenarios and generalizes well to objects and movement paths not included in training. This advancement holds promise for enhancing robotic dexterity and reliability across various applications, including healthcare (handling surgical tools), manufacturing (assembling delicate parts), logistics (sorting awkward packages), and home assistance. The study highlights the importance of