Tactile sensing is an essential component in human–robot interaction and object manipulation. Soft sensors allow for safe interaction and improved gripping performance. Here we present the TacTip family of sensors: a range of soft optical tactile sensors with various morphologies fabricated through dual-material 3D printing. All of these sensors are inspired by the same biomimetic design principle: transducing deformation of the sensing surface via movement of pins analogous to the function of intermediate ridges within the human fingertip. The performance of the TacTip, TacTip-GR2, TacTip-M2, and TacCylinder sensors is here evaluated and shown to attain submillimeter accuracy on a rolling cylinder task, representing greater than 10-fold super-resolved acuity. A version of the TacTip sensor has also been open-sourced, enabling other laboratories to adopt it as a platform for tactile sensing and manipulation research. These sensors are suitable for real-world applications in tactile perception, exploration, and manipulation, and will enable further research and innovation in the field of soft tactile sensing.

Although superresolution has been studied to huge impact in visual imaging, it is relatively unexplored in tactile robotics. Here we demonstrate a novel optical sensor design (the TacTip) capable of achieving 40-fold localization superresolution to 0.1mm accuracy compared with a 4mm resolution between tactile elements. This superresolution is reached for localizing a 40mm diameter hemicylinder with a tactile finger pad also of 40mm diameter. Deformations of the sensor surface are measured as displacements of molded internal pins, with pin separation thus defining sensor resolution. Active Bayesian perception for classifying object location was used to ensure robust localization and hence the magnitude of the superresolution. These results are comparable with those for capacitive tactile sensors, which we interpret as originating from a convergence in the taxel-based design of the optical sensor and capacitive tactile sensors. The attained superresolution is comparable to the best perceptual hyperacuity in humans.

The use of tactile feedback for precision manipulation in robotics still lags far behind human capabilities. This study has two principal aims: 1) to demonstrate in-hand reorientation of grasped objects through active tactile manipulation; and 2) to present the development of a novel TacTip sensor and a GR2 gripper platform for tactile manipulation. Through the use of Bayesian active perception algorithms, the system successfully achieved inhand reorientation of cylinders of different diameters (20, 25, 30, and 35 mm) using tactile feedback. Average orientation errors along manipulation trajectories were below 5° for all cylinders with reorientation ranges varying from 42° to 67°. We also demonstrated an improvement in active tactile manipulation accuracy when using additional training data. Our methods for active tactile manipulation with the GR2 TacTip gripper are model free, can be used to investigate principles of dexterous manipulation, and could lead to essential advances in the areas of robotic tactile manipulation and teleoperated robots.

Tactile manipulation will be essential for automating industrial and service tasks currently done by humans. However, the application of tactile feedback to dexterous manipulation remains a challenging unsolved problem, with robot capabilities lagging far behind those of humans. Here, we present the tactile thumb (TacThumb): a cheap, robust, 3-D-printed optical tactile sensor integrated on the Yale GrabLab model M2 gripper. To test tactile manipulation capabilities, a cylinder is rolled along the TacThumb using the opposing nontactile finger. The tactile information permits localization of the test cylinder along the TacThumb to submillimeter accuracy over most of the movement range. In consequence, the M2 gripper can perform accurate inhand tactile manipulation, by providing information that can be used to control the location of the test object within the hand. Tactile manipulation is demonstrated by rolling cylinders with a range of diameters up and down the TacThumb along a target trajectory, using only tactile data to update its current position and move it toward a target. This model-free approach gives a demonstration of basic tactile manipulation without the need for a kinematic model of the hand, in a manner that should generalize to other tactile manipulation tasks.

The fingerprint is a morphological aspect of the human fingertip that has interesting implications for our sense of touch. Previous studies focused on how the fingerprint affects the perception of stimuli that excite high temporal frequencies, such as for texture perception. These studies also only add papillary ridges to their sensors. Here, we endow a biomimetic sensor with both papillary ridges (fingerprint) and a dermal stiffness contrast (stiffer intermediate ridges), and assess the impact on localization perception accuracy. The sensor is based on a novel modular version of a three-dimensional printed tactile sensor (TacTip). Tactile data were collected with these tips on nine stimuli with varying curvature. The location perception acuity of three tips [smooth, fingerprint, and fingerprint (cores)] were compared with a probabilistic classification method, finding that both fingerprinted tips increase the perceptual acuity of small spatial scales. Interestingly, the fingerprint variant had poorer accuracy than the smooth tip for larger spatial scales; however, adding cores to enhance the dermal stiffness counteracted the degradation of accuracy. This supports the theories that the fingerprint aids the classification of edges and smaller spatial scales, and demonstrates that the addition of a fingerprint to an artificial tactile sensors improves its acuity.

Tactile perception methods rely on creating mappings from tactile data to percepts. In many approaches to artificial tactile perception, this involves extensive sampling of the object during the training phase. We introduce here a method to instead generalize tactile features across different orientations. This method is applied to the TacTip v2, a three-dimensional printed optical tactile sensor with internal pins acting as taxels arranged with a 12-fold rotational symmetry. By rotating a small sample of tactile images, we are able to generalize tactile stimuli to new orientations. The method was validated across several tactile stimuli on an edge orientation classification task. Data were then generalized for a combination of orientations and locations of one of these stimuli, and this dataset was used as the basis for an exploratory control task: contour following around a circular disk. The generalization method leads to a strong reduction in the time needed to gather training data and only a moderate increase in classification error, and is particularly suited for multidimensional tactile data sampling in complex tasks such as tactile manipulation. We expect the method to generalize well to other tactile stimuli.