As robots move toward more complex environments, imbuing them with a sense of touch similar to humans becomes increasingly important. To fulfill that goal, there has been significant research conducted in the past few decades to develop a tactile sensor that matches human level touch capabilities. Recently, the progress in capacitive touch screens has made capacitive sensing a very appealing option for such a sensor, and therefore many research groups have proposed novel designs of tactile sensors based on capacitive technologies. This technology has the advantage of generating a predictable sensor response with a high degree of sensitivity, but has the drawback of a limited spatial resolution. This paper shows how using mutual capacitance in combination with a microstructured dielectric can lead to a very sensitive sensor that also possesses a high spatial resolution. The response of the sensor in relation to its various components is explored in order to fully comprehend the physical principles of the sensing mechanism and generate a predictable output.

Research around tactile sensing for grasp stability prediction in robotic manipulators continues to be popular, however few works are able to achieve a high classification accuracy. Due to simulation complexity, data-driven methods are often forced to rely on experimental data, yielding small, often unbalanced, data sets. In this work, the authors use a 3972 sample data set to explore the effects of the data set composition on the performance of a classifier. While maintaining a similar overall accuracy, the ability to recognize a grasp failure was significantly impacted by the composition of the data set. The authors propose an autonomous pipeline designed to generate more diverse failure grasps. On failure-rich data, a tactile-based classifier with a balanced training set achieved a classification accuracy of 84.68% while maintaining a recall of the grasp failure class of 76%. This represents a 71.79% improvement in recall over a model trained on a larger but unbalanced data set.

While collaborative robotic arms offer significant safety benefits, safety of the overall manipulator system cannot be guaranteed unless equally strict safety requirements are satisfied by the accompanying end-effector. Current robot grippers are not made in a way that fulfills such a requirement, resulting in collaborative robots needing to operate in a protected environment. This paper presents a novel permanent magnet actuator inside of a conventional industrial electric gripper which results in an end-effector that has an unmatched force range of 1-2N to 43N and exhibits interesting characteristics suited to the requirements of a safe gripper such as torque holding without power, variable stiffness and force sensing.

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.

The growing demand in industry for robots capable of performing a variety of tasks requires an increased capability in robotic grasping. Humans are adept at interacting with novel objects, a skill attributed primarily to tactile feedback in the form of exteroception and proprioception. This paper presents a novel way to incorporate exteroception and proprioception into grasp stability assessment: by using convolutional neural networks. This method improves upon the results of a unsupervised feature learning approach that used similar tactile feedback. 1000 different grasps on 100 objects were used to train and test the network. The network achieved an overall accuracy of 88.4% while predicting the failure class with an accuracy of 92.7%.

Today, collaborative robots are often taught new tasks through “teaching by demonstration” techniques rather than manual programming. This works well for many tasks; however, some tasks like precise tight-fitting insertions can be hard to recreate through exact position replays because they also involve forces and are highly affected by the robot's repeatability and the position of the object in the hand. As of yet there is no way to automatically detect when procedures to reduce position uncertainty should be used. In this paper, we present a new way to automatically detect insertion tasks during impedance control-based trajectory teaching. This is accomplished by recording the forces and torques applied by the operator and inputting these signals to a convolutional neural network. The convolutional neural network is used to extract important features of the spatio-temporal forces and torque signals for distinguishing insertion tasks. Eventually, this method could help robots understand the tasks they are taught at a higher level. They will not only be capable of a position-time replay of the task, but will also recognize the best strategy to apply in order to accomplish the task (in this case insertion). Our method was tested on data obtained from 886 experiments that were conducted on eight different in-hand objects. Results show that we can distinguish insertion tasks from pick-and-place tasks with an average accuracy of 82%.

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.

This paper describes the design and manufacturing of soft artificial skin with an array of embedded soft strain sensors for detecting various hand gestures by measuring joint motions of five fingers. The proposed skin was made of a hyperelastic elastomer material with embedded microchannels filled with two different liquid conductors, an ionic liquid and a liquid metal. The ionic liquid microchannels were used to detect the mechanical strain changes of the sensing material, and the liquid metal microchannels were used as flexible and stretchable electrical wires for connecting the sensors to an external control circuit. The two heterogeneous liquid conductors were electrically interfaced through flexible conductive threads to prevent the two liquid from being intermixed. The skin device was connected to a computer through a microcontroller instrumentation circuit for reconstructing the 3-D hand motions graphically. The paper also presents preliminary calibration and experimental results.

Interest in low-cost force-torque sensors with high performance will continue to increase in the next years, as robot control needs to rely on more sensors to move into the human environment. Existing force-torque sensors still suffer from some shortcomings such as noise sensitivity, low resolution and high cost. This limits their use in some emerging applications. Through an advanced systematic design method based on a symbolic formulation of the wrench-displacement relationship, we designed compact and cost-effective three-axis capacitance-based force sensor. Despite its relative simplicity, our sensor exhibits a very good sensitivity thanks to the electronic components selected, but also to a special soft silicone layer filled with nanoparticles of ferroelectric ceramic. This composite we made has a relatively high dielectric constant. This paper presents the design process that led to the sensor including the structure, the capacitance measurement circuit and the fabrication of the soft dielectric. Characterization tests have also been carried out on a prototype of the sensor and the results show that it is sensitive and easy to fabricate.

We describe an underactuated hand mechanism that is able to adopt a wide range of grasp types by varying the internal forces in its fingers. The adjustment is accomplished by varying the preloads of springs, which affect the grasp stability and stiffness for large and small objects. Preload adjustment can be accomplished with low power, non-backdrivable actuators in the fingers. The analysis is presented first for a planar, two-fingered hand to illustrate the trends and tradeoffs associated with variations in preload. The results are then applied numerically to a three fingered hand with three phalanges per finger. This design is a prototype for a hand to be used in an underwater oil drilling platform under conditions of low friction and uncertain object locations.

Providing robots with the capability of sensing their surrounding environment is an important feature that would lead to a more intuitive and safe physical human-robot interaction. This paper proposes a new design of homogeneous flexible and stretchable robot skin based on carbon-black-filled (CBF) silicone and conductive fabric that can sense multiple contact locations as well as applied pressure. CBF silicone has been already used in sensing technology but its piezoresistivity is still largely misunderstood. This particular behavior is investigated in this paper through a set of experiments conducted on isolated sensing cells. Using the results of these experiments, a model describing the variation of the resistivity in the CBF silicone as a function of the applied pressure is proposed. Based on this model, a simple way to accurately estimate the applied pressure in real time is demonstrated. Finally, using this improved knowledge of the behaviour of the CBF silicone, the fabrication of a fully functional sensor array is presented. The proposed design has the particularity of circumventing the well-known problem of cross-talk between sensing cells.

For physical human-robot interaction, safety and dependability are of utmost importance due to the potential risk a relatively powerful robot poses for human beings. From the control standpoint, it is possible to increase this level of safety by guaranteeing that the robot will never exhibit any unstable behaviour. However, stability is not the only concern in the design of a controller for such a robot. During human-robot interaction, the resulting cooperative motion should be truly intuitive and should not restrict in any way the human performance. For this purpose, we have designed a new variable admittance control law that guarantees the stability of the robot during constrained motion and also provides a very intuitive human interaction. The first characteristic is provided by the design of a stability observer while the other is based on a variable admittance control scheme that uses the force derivative as a way to predict human intention. The stability observer is based on a previous stability investigation of cooperative motion which implies the knowledge of the interaction stiffness. A method to accurately estimate this stiffness online using the data coming from the encoder and from a multi-axis force sensor at the end effector is also provided. The stability and intuitivity of the control law were verified in a user study during a cooperative drawing task with a 3 degree-of-freedom (dof) parallel robot.

Providing contact sensing on the whole body of a robot is a key feature to increase the safety level of physical human-robot interaction. In this paper, a new robot skin capable of sensing multiple contact locations is presented. The motivation behind the proposed design is to produce a relatively inexpensive skin having the capability to provide the spatial location of collisions and also to add compliance to the robot's external cover. The resulting device is a thin flexible sensor sheet made of polyimide films with electrically conductive ink and a pressure sensitive conductive rubber sheet. The problem of internal wire routing is circumvented by the use of conductive ink and a circuit is proposed to minimize the number of output wires. To provide collision absorption and mechanical robustness, the sensor is embedded in different layers of polyurethane using shape deposition manufacturing (SDM). The paper presents the design and the fabrication process of the skin but also some experimental results on the determination of the mechanical properties of the resulting sensor as well as its potential for increasing human safety during human robot interaction.

For human-robot cooperation in the context of human-augmentation tasks, the stability of the control model is of great concern due to the risk for the human safety represented by a powerful robot. This paper investigates stability conditions for impedance control in this cooperative context and where touch is used as the sense of interaction. The proposed analysis takes into account human arm and robot physical characteristics, which are first investigated. Then, a global system model including noise filtering and impedance control is defined in a state-space representation. From this representation, a Lyapunov function candidate has been successfully discovered. In addition to providing conclusions on the global asymptotic stability of the system, the relative simplicity of the resulting equation allows the derivation of general expressions for the critical values of impedance parameters. Such knowledge is of great interest in the context of design of new adaptive control laws or simply to serve as design guidelines for conventional impedance control. The accuracy of these results were verified in a user study involving 7 human subjects and a 3-dof parallel robot. In this experiment, the real effective stability frontier was defined for each subject and compared with values predicted using the Lyapunov function.

This paper is a contribution to an invited session on the benchmarking of parallel mechanisms. The aim of the session is to compare different existing designs and prototypes of parallel mechanisms using a common set of benchmarking criteria. First, the kinematic architectures of parallel mechanisms of the multipteron family are presented. In addition to the tripteron and the quadrupteron, the pentapteron, a five-degree-of-freedom (dof) parallel mechanism is introduced. Then, the benchmarking criteria are applied to the prototypes of the tripteron (3-dof) and the quadrupteron (4-dof) prototypes. Although the tripteron and quadrupteron parallel mechanisms have been presented elsewhere, their properties, highlighted by the benchmarking analysis presented here are revealed for the first time.