Tactile sensing provides crucial information about the stability of a grasped object by a robotic gripper. Tactile feedback can be used to predict slip, allowing for timely response to perturbations and to avoid dropping objects. Tactile sensors, included in robotic grippers, measure vibrations, strain or shearing forces which are produced by the movement of the grasped object. With sufficient spatial resolution, tactile sensors can even classify slip or estimate the 3d force displacement field. However, current tactile sensors fail to preemptively detect slippage, requiring fast reaction times during applications in real-time control. Here we show a perception framework that can predict slippage before it occurs by estimating the frictional safety margin. The safety margin indicates the margin to the frictional strength of a grasp, which decreases for reduced friction or increased load force. An accurate safety margin estimate allows for more efficient robot grip force control while providing robustness against object uncertainty and frictional conditions. We developed a high resolution tactile sensor, on which we trained a convolutional neural network to learn the relationship between tactile images and the safety margin. The network’s performance is evaluated on unseen test data, showing robustness to variations in environmental conditions. The results demonstrate that the tactile images contain the information needed to produce accurate safety margin estimates. These estimates can be used for control up to 20% of the minimum required grip force, mimicking human grasping behavior. This approach can drive new grasp control methods and enable robotic grasping of fragile objects in highly dynamic environments. Applications can be found in harvesting, parcel sorting, or improving human-robot interaction.

There are many stages that involve humans handling food objects in the processing chains from farms to stores. For some of these tasks it is desirable to look for a robotic solution to either assist the human or even take over that task, e.g. if it is physically demanding, imposes contamination risks or because of economical considerations. Moreover, recently the COVID-19 pandemic revealed even more vulnerabilities in our food processing chains, when seasonal labourers where blocked at borders and some food processing sites turned out to be "corona hotspots" with the result that whole food processing chains were disturbed. A step towards solving some of these problems is studying robotic grasping, since it is a crucial skill for many manipulation tasks. This work focuses on force closure grasps for pick-and-place tasks. Since humans grasp novel objects effortlessly, a human inspired approach is proposed that combines visual and tactile sensory information and learns from its mistakes thanks to reinforcement learning. Visual features obtained from an RGB-D camera in combination with pressure information from sensors on the finger tips of the robotic hand are used to set the desired grasp force adaptively while holding the object. A novel algorithm is proposed for learning to grasp with minimal force: LIFT (Learning of Initial Force and Tuning). The novel grasp approach is evaluated in a Gazebo simulation environment and on a real-world robotic setup. The approach results in successful grasping in both simulation and on the real-world setup in tens or hundreds of learning interactions, depending on the size of the state-action space. Success rates of 96.3 % and 96.7 % are obtained in simulation and on the real-world setup, respectively. The results from the experiments indicate that the approach is successful in grasping with minimal force.