To gently grasp objects, robots need to balance generating enough friction yet avoiding too much force that could damage the object. In practice, the force regulation is challenging to implement since it requires knowledge of the friction coefficient, which can vary from object to object and even from grasp to grasp. Tactile sensing offers a window in the contact mechanics and provides information about friction. Notably touch can detect the precursor of the object slipping away from the grasp. To find this information, tactile sensors measure the deformation field of an artificial skin in both the normal and tangential direction. However, current approaches only react to slip and therefore react too late to perturbations. The object slips, inducing a failure of the grasp and damage. In this study, we introduce a method that uses machine-learning to anticipate slip by computing the so-called safety margin of the grasp. This safety margin represents the extra lateral force that maintains the contact away from the frictional limit. To find this value, we use a high-density camera-based tactile sensor to measure the 3D deformation of the surface via the movement of 82 colored markers. We trained a Convolutional Neural Network (CNN) to estimate the safety margin from the tactile images. Because it gives a distance to slip, the safety margin is a powerful metric for regulating grasp forces. As a testament of this effectiveness, we show that a simple proportional controller can robustly grasp a wide variety of objects. The results show that this control method outperforms slip detection methods, by reducing regrasp reaction times while decreasing grasping forces to 1-3 N.

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Shortly after touching an object, humans can tactually gauge the frictional resistance of a surface. The knowledge of surface friction is paramount to tactile perception and the motor control of grasp. While potent correlations between friction and participants' perceptual response have been found, the causal link between the friction of the surface, its evolution and its perceptual experience has yet to be demonstrated. Here, we leverage new experimental apparatus able to modify friction in real time, to show that participants can perceive sudden changes in friction when they are pressing on a surface. Surprisingly, only a reduction of the friction coefficient leads to a robust perception. High-speed imaging data indicate that the sensation is caused by a release of a latent elastic strain over a 20 ms timeframe after the activation of the friction-reduction device. This rapid change of frictional properties during initial contact is interpreted as a normal displacement of the surface, which paves the way for haptic surfaces that can produce illusions of interacting with mechanical buttons.

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When grasping objects, we rely on our sense of touch to adjust our grip and react against external perturbations. Less than 200 ms after an unexpected event, the sensorimotor system is able to process tactile information to deduce the frictional strength of the contact and to react accordingly. Given that roughly 1,300 afferents innervate the fingertips, it is unclear how the nervous system can process such a large influx of data in a sufficiently short time span. In this study, we measured the deformation of the skin during the initial stages of incipient sliding for a wide range of frictional conditions. We show that the dominant patterns of deformation are sufficient to estimate the distance between the frictional force and the frictional strength of the contact. From these stereotypical patterns, a classifier can predict if an object is about to slide during the initial stages of incipient slip. The prediction is robust to the actual value of the interfacial friction, showing sensory invariance. These results suggest the existence of a possible compact set of bases that we call Eigenstrains. These Eigenstrains are a potential mechanism to rapidly decode the margin from full slip from the tactile information contained in the deformation of the skin. Our findings suggest that only 6 of these Eigenstrains are necessary to classify whether the object is firmly stuck to the fingers or is close to slipping away. These findings give clues about the tactile regulation of grasp and the insights are directly applicable to the design of robotic grippers and prosthetics that rapidly react to external perturbations.@en

Tactile sensing can provide access to information about the contact (i.e. slippage, surface feature, friction), which is out of reach of vision but crucial for manipulation. To access this information, a dense measurement of the deformation of soft fingertips is necessary. Recently, tactile sensors that rely on a camera looking at a deformable membrane have demonstrated that a dense measurement of the contact is possible. However, their manufacturing can be time-consuming and labor-intensive. Here, we show a new design method that uses multi-color additive manufacturing and silicone casting to efficiently manufacture soft marker-based tactile sensors that are able to capture with high-resolution the three-dimensional deformation field at the interface. Each marker is composed of two superimposed color filters. The subtractive color mixing encodes the normal deformation of the membrane, and the lateral deformation is found by centroid detection. With this manufacturing method, we can reach a density of 400 markers on a 21 mm radius hemisphere, allowing for regular and dense measurement of the deformation. We calibrated and validated the approach by finding the curvature of objects with a threefold increase in accuracy as compared to previous implementations. The results demonstrate a simple yet effective approach to manufacturing artificial fingertips for capturing a rich image of the tactile interaction at the location of contact.@en

The contact between the fingertip and an object is formed by a collection of micro-scale junctions, which collectively constitute the real contact area. This real area of contact is only a fraction of the apparent area of contact and is directly linked to the frictional strength of the contact (i.e., the lateral force at which the finger starts sliding). As a consequence, a measure of this area of real contact can help probe into the mechanism behind the friction of skin on glass. In this article, we present two methods to measure the variations of contact area;one that improves upon a tried-and-true fingertip imaging technique to provide ground truth, and the other that relies on the absorption and reflection of acoustic energy. To achieve precise measurements, the ultrasonic method exploits a recently developed model of the interaction that incorporates the non-linearity of squeeze film levitation. The two methods are in good agreement (= 0.94) over a large range of normal forces and vibration amplitudes. Since the real area of contact fundamentally underlies fingertip friction, the methods described in the article have importance for studying human grasping, understanding friction perception, and controlling surface-haptic devices.

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Humans efficiently estimate the grip force necessary to lift a variety of objects, including slippery ones. The regulation of grip force starts with the initial contact and takes into account the surface properties, such as friction. This estimation of the frictional strength has been shown to depend critically on cutaneous information. However, the physical and perceptual mechanism that provides such early tactile information remains elusive. In this study, we developed a friction-modulation apparatus to elucidate the effects of the frictional properties of objects during initial contact. We found a correlation between participants’ conscious perception of friction and radial strain patterns of skin deformation. The results provide insights into the tactile cues made available by contact mechanics to the sensorimotor regulation of grip, as well as to the conscious perception of the frictional properties of an object.

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