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Grasping objects whose physical properties are unknown is still a greatchallenge in robotics. Most solutions rely entirely on visual data to plan thebest grasping strategy. However, to match human abilities and be able toreliably pick and hold unknown objects, the integration of an artificial senseof touch in robotic systems is pivotal. This paper describes a novelmodel-based slip detection pipeline that can predict possibly failing grasps inreal-time and signal a necessary increase in grip force. As such, the slipdetector does not rely on manually collected data, but exploits physics togeneralize across different tasks. To evaluate the approach, a state-of-the-artvision-based tactile sensor that accurately estimates distributed forces wasintegrated into a grasping setup composed of a six degrees-of-freedom cobot anda two-finger gripper. Results show that the system can reliably predict slipwhile manipulating objects of different shapes, materials, and weights. Thesensor can detect both translational and rotational slip in various scenarios,making it suitable to improve the stability of a grasp.

Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor