Robotic manipulators navigating cluttered shelves or cabinets may find it challenging to avoid contact with obstacles. Indeed, rearranging obstacles may be necessary to access a target. Rather than planning explicit motions that place obstacles into a desired pose, we suggest allowing incidental contacts to rearrange obstacles while monitoring contacts for safety. Bypassing object identification, we present a method for categorizing object motions from tactile data collected from incidental contacts with a capacitive tactile skin on an Allegro Hand. We formalize tactile cues associated with categories of object motion, demonstrating that they can determine with > 90% accuracy whether an object is movable and whether a contact is causing the object to slide stably (safe contact) or tip (unsafe).

The 2018 IROS Fan Robotic Challenge tasked participants with programming a robot to autonomously open and close a Spanish folding fan, highlighting the obstacles still associated with the dexterous manipulation of objects for robotic systems. Since high DoFs grippers are complex to coordinate and overkill for many industrial processes, our approach used an under-actuated parallel gripper with a 3D-printed adaptation to precisely grasp the fan in such a manner that gravity could be leveraged to act on the fan to produce an extrinsic, or external, dexterity. With our approach, we completed the challenge in 12.38 seconds, resulting in a top three finish. Furthermore, using a multi-modal tactile sensor, we analyzed the vibrations in the grasp during the manipulation and were able to distinguish the opening and closing of the fan from the motion of the robot with a 83% accuracy.

Grasping tasks have always been challenging for robots, despite recent innovations in vision-based algorithms and object-specific training. If robots are to match human abilities and learn to pick up never-before-seen objects, they must combine vision with tactile sensing. This paper present a novel way to improve robotic grasping: by using tactile sensors and an unsupervised feature-learning approach, a robot can find the common denominators behind successful and failed grasps, and use this knowledge to predict whether a grasp attempt will succeed or fail. This method is promising as it uses only high-level features from two tactile sensors to evaluate grasp quality, and works for the training set as well as for new objects. In total, using a total of 54 different objects, our system recognized grasp failure 83.70% of time.

As technology develops, manufacture process becomes more and more automated using robots. There is demand for high performance tactile sensor which can support robotic grippers in manipulation tasks especially for unstructured flexible objects. Despite the efforts that have been spent, the fabrication process of those functional sensor remains complicated due to their requirement of specialized materials and equipment. The proposed multimodal sensor overcomes the difficulty by enhancing the electrical and mechanical design therefore simplifying the manufacture steps. In this version, static and dynamic sensing are integrated in the same layer of capacitive sensor with direct written microstructured dielectric. This structure allows it to have large range of force sensing as well as the ability of detecting contact events such as slippage or losing of contact.

This paper explores the use of haptic and visual feedback during routine object-manipulation tasks. We conducted tests of slippage detection, contact detection, and grasp control, with the help of twelve human participants. During the tests, the subjects controlled the robotic arm of a UR5 Universal Robot that was mounted with a three-finger robotic gripper and equipped with tactile sensors. The participants used either their visual feedback, vibrotactile feedback, or pressure feedback to assess the movements of the robotic fingers as they attempted to complete each task. We analyzed their performance with each type of feedback in order to evaluate how haptic feedback may be used to reduce the need for visual attention, and thus improve the lives of upper-limb amputees.

Robotic operations that involve the displacement of objects generate different kinds of dynamic events. These may simply correspond to normal robot-related motion, or contact(s) with the object(s) during grasping, but they may also be potentially-problematic events like slippage. In this paper, we use sparse data from tactile sensors to detect slippage and discriminate object-gripper slip from object-world slip. The method we propose can also identify vibrations that correspond to other dynamic events automatically, even when those events are not related to slippage. The tactile data can then be classified, allowing the robot to react accordingly. To achieve this goal, we compute the power spectral density (PSD) of the tactile dynamic signal, and we apply transformations to the PSD that were inspired by the automatic speech recognition (ASR) field. The originality of this work comes from using a sparse representation of the transformed data to obtain sparse vectors containing a small set of high-level features. Those sparse vectors are then used as inputs to a simple linear support vector machine (SVM), that acts as a classifier and quickly estimates the event to which they correspond. Our method was tested on data obtained from 244 experiments that were conducted on 32 different everyday-objects. Results show that we can successfully discriminate most of the dynamic events we studied in this work. Moreover, by using this technique, we are able to detect slippage with an accuracy of 92.60% and to differentiate object-gripper slip from object-world slip with a success rate of 89.42%.

This paper presents a robotic system that was used to study the restoration of touch sensitivity. This approach could improve the lives of people who suffer from having lost organs or upper-limbs. Here, a combination of tactile sensors, robotic fingers, and a haptic interface enabled us to undertake different types of experiments on human subjects. To this end, we have conducted two separate tests on eight human subjects in order to assess the effectiveness of the static and dynamic modalities in different detectable ranges of the skin sensitivity. As of now, overall results show the relative functionality of the proposed mechanism for further experimental research. Under a static condition we received better restitution feedback at the lowest and highest magnitude levels, compared to the two in-between levels. The lowest and highest magnitude levels, 2N and 8N respectively, had an overall success rate of 75%, while the two middle levels, 4N and 6N, had a success rate of 43%. Under a dynamic condition, results showed that the whole system could adequately convey texture information via vibrations, and that it allowed subjects to successfully differentiate textures 78.33% of the time.