# Detecting and Controlling Slip through Estimation and Control of the Sliding Velocity
## 1 Introduction  
Humans can grasp and manipulate a variety of objects with dexterity and safety without knowing a priori their physical properties by perceiving the motion of the object upon contact with the fingertips by means of the sense of touch [1]. On the basis of tactile perception, they control the magnitude of the friction force and torque by acting on the grasp force, which the fingertips exert on the object, thereby avoiding grasp failure. Thus, tactile sensing is of paramount importance for humans to interact with the environment. In the last two decades, many designs of tactile sensors have been proposed in the scientific literature by robotics researchers with the aim of mimicking the human sense of touch. Comprehensive reviews of tactile sensing systems for dexterous robotic hands can be found in [2, 3]. The proposed solutions are based on two alternative concepts: sensors based on an array of independent contacts or tactile receptors that easily allow for the identification of contact location and sensors based on a continuous deformable medium between the contact and the receptors, which allows a few receptors to interpolate the information among them. The latter are more frequently used to measure contact force intensity or magnitude. Both sensor principles may be supported by different transduction technologies, e.g., resistive, capacitive and magnetic, which can be selected depending on their costs, weights, integration levels, and dimensions. The perception of the torsionalmoment is a key requirement to select the appropriate tactile sensor to securely grasp an object when it is subject not only to force but also to moment loads. Only a few sensors are capable of measuring the full 6-D wrench; moreover, sensors that are also able to measure torque, e.g., the 6-axis version by Optoforce, use an array of 3-D sensors coupled with a rigid surface that limits the frictional torque that the fingertip can apply to the grasped object. Recently, the GelSight visuotactile sensor, which uses a vision system to translate the deformation of the gel layer caused by the contact with the environment into tactile information [4; 5] has been proposed to estimate shear force and contact geometry. A review of visuotactile sensors with an emphasis on GelSight can be found in [6]. In this paper, we use the SUNTouch force/tactile sensor [7; 8] developed in our robotics lab. The SUNTouch force/tactile sensor has been demonstrated to be able to provide the grasp controller with accurate measurements of the 6-D contact wrench [9]. Mimicking human ability, slip detection, slipping avoidance and controlled sliding are key facets of making robots capable of grasping and manipulating objects. This means that the robot should be able to detect the incipient grasp failure and control the grasp force to avoid it without damaging the grasped object, or, to change the object/fingertip relative position, to execute controlled sliding of the fingertips without losing contact with the object. Several methods of slip detection have been proposed in the last decade [10]. The approaches can be classified as model-based and model-free approaches. In [11], using the BioTac sensor and resorting to the friction cone concept, the authors compared various slip detection methods. However, slipping detection algorithms based on the friction cone concept fail when a torsional load acts on the grasped object. In [9], the authors presented a model-based approach to slipping detection and avoidance in the presence of both shear and torsional load by resorting to the concept of the limit surface [12]. Model-free approaches, the prevailing trend in recent years, refer to learning-based slip detection methods, mostly based on the use of visuotactile sensors such as GelSight and TacTip. In [13; 14], the authors proposed a slip classification framework by resorting to a convolutional long short-term memory network.