Soft grippers offer gentle interaction with objects, significantly reducing the risk of damage. Their compliance in both material and structure allows them to adapt to a wide variety of object shapes and sizes. However, the deformable nature of soft grippers poses challenges for integrating precise proprioception and tactile sensing, especially when aiming for large-area high-resolution tactile perception. In nature, the elephant's trunk exemplifies an ideal combination of compliance and tactile sensitivity, enabling it to delicately manipulate diverse objects without causing damage while also exploring its surroundings through touch. Inspired by this, we present EleTac, a soft vision-based tactile gripper that enables safe object grasping via a pinch-like motion and delivers high-resolution full-surface tactile feedback for integrated proprioceptive and exteroceptive sensing. Experimental results demonstrate that even with a simple control strategy, EleTac can robustly grasp and lift a variety of objects, exhibiting strong adaptability and generalization. Furthermore, the seamless integration of grasping and tactile sensing capabilities facilitates practical applications, including exploration and excavation in granular media, as well as adaptive surface following. Overall, EleTac validates the 'manipulator-as-sensor' design philosophy, achieving high-quality tactile feedback without requiring additional sensing modules.

While wearable robots that utilize intrinsically soft materials for actuation offer enhanced safety and biological compatibility, the challenges of sensing and control significantly affect their performance. The control problem in such systems is inherently complex, and the inclusion of 'softness' introduces additional nonlinearities, hysteresis, and uncertainties. Furthermore, the effectiveness of control strategies is highly dependent on sensor selection and integration, which presents its own challenges. Most robotic systems require separate sensors for control purposes. In this study, a new sensing and control scheme are introduced for soft wearable robots, leveraging the intrinsic soft-sensing capability of fluidic filament actuators without adding computational complexity. This method enables simultaneous sensing and actuation with$\mathbf{9 6 \%}$position accuracy, even under physical disturbances. This approach is demonstrated with a soft assistive device for elbow flexion/extension, achieving 70.5% tracking accuracy and a 0.09s response delay to human intention, ensuring the system provides minimal resistance when assistance is not needed, while delivering the required support when necessary.

Soft-bodied robots with multimodal sensing capabilities hold promise for versatile and user-friendly robotics. However, seamlessly integrating multiple sensing functionalities into soft artificial skins remains a challenge due to compatibility issues between soft materials and conventional electronics. While vision-based tactile sensing has enabled simple and effective sensor designs for robotic touch, there has been limited exploration of this technique for intrinsic multimodal sensing in large-sized soft robot bodies. To address this gap, this paper introduces a novel vision-based soft sensing technique, named ProTac, capable of operating either in tactile or proximity sensing modes. This vision-based sensing technology relies on a soft functional skin that can actively switch its optical properties between opaque and transparent states. Furthermore, the paper develops efficient learning pipelines for proximity and tactile perceptions, as well as sensing strategies enabled through the timing activation of the two sensing modes. The effectiveness of the soft sensing technology is demonstrated through a soft ProTac link, which can be integrated into newly constructed or existing commercial robot arms. Results suggest that robots integrated with the ProTac link, along with rigorous control formulation can perform safe and purposeful control actions, which enhances human-robot interaction scenarios and facilitates motion control tasks that are challenging to achieve with conventional rigid links. Supplementary video:https://youtu.be/dFgZLUpeWw4Project website:https://quan-luu.github.io/protac-website/

In this article, we present a design concept, in which a monolithic soft body is incorporated with a vibration-driven mechanism, calledLeafbot. We first report a morphological design of the robot's limbs that facilitates the forward locomotion of our vibration-driven model and enhances the capability of coping with sloped obstacles and irregular terrains. Second, the fabrication technique to achieve such a soft monolithic structure and limb morphology is fully addressed. Third, we clarify the locomotion of the Leafbot under high-frequency excitation via analytical and empirical methods in flat and even surface conditions. The maximum attained velocity in such a condition is 5 body length/ second. Finally, three model designs are constructed, each featuring a different limb pattern. We examine the terradynamics characteristics of three patterns in three pre-defined conditions, i.e., the success rate of overcoming the slope, semi-circular obstacles, and step-field terrains specialized by the rugosity factor. This proposed investigation aims to build a foundation for further terradynamics study of vibration-driven soft robots in a more complicated and confined environment, with potential applications in inspection tasks.

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

Recent developments in vision-based tactile sensing offer a simple means to enable robots to perceive touch interactions. However, existing sensors are primarily designed for small-scale applications like robotic hands, lacking research on their integration for large-sized robot bodies that can be leveraged for safe human-robot interactions. This paper explores the utilization of the previously-developed vision-based tactile sensing link (called TacLink) with soft skin as a safety control mechanism, which can serve as an alternative to conventional rigid robot links and impact observers. We characterize the behavior of a robot integrated with the soft TacLink in response to collisions, particularly employing a reactive control strategy. The controller is primarily driven by tactile force information acquired from the soft TacLink sensor through a data-driven sim2real learning method. Compared with a standard rigid link, the results obtained from collision experiments also confirm the advantages of our "soft" solution in impact resilience and in facilitating controls that are difficult to achieve with a stiff robot body. This study can act as a benchmark for assessing the efficiency of soft tactile-sensitive skins in reactive collision responses and open new safety standards for soft skin-based collaborative robots in human-robot interaction scenarios.

Tasks involving exploration and inspection of narrow environments demand a robot to have a flexible body. Such a robot is especially preferred if the integrity of its surrounding is crucial, as in endoscopy procedures. We propose the design of a small, self-propelled soft robot that can operate in a constrained environment. By periodic activation of a series of pneumatic actuators fabricated using a casting technique, sinusoidal locomotion is achieved. The wave-like locomotive strategy with an additional support mechanism enabled movement in multiple scenarios, including traveling horizontally and vertically in environments of different characteristics. Two analytical models are presented to highlight the design characteristics. The first predicts the velocity of the robot in relation to the working conditions, while the second calculates the force that the robot body exerts on its surroundings. Its mobility was tested in simple and complex routes under rigid and elastic environments. The resulting percent errors for the predictions of velocity and lateral force are 7.89% and 16.86%, respectively. In terms of performance, the robot can move horizontally in rigid tubes even if the walls are lubricated, and can achieve a peak speed of 40.11 mm/s, or 0.171 Body-Length/s (BL/s). With the addition of a tail balloon, the robot also successfully ascended a vertical tube with a maximum speed of 9.22 mm/s (or 0.039 BL/s). The presented work is expected to pave the way toward feasible robotic applications, such as pipe inspection.

Following biology's lead, soft robotics has emerged as a perfect candidate for actuation within complex environments. While soft actuation has been developed intensively over the last few decades, soft sensing has so far slowed to catch up. A largely unresearched area is the change of the soft material properties through prestress to achieve a degree of mechanical sensitivity tunability within soft sensors. Here, a new 3D force sensor which employs novel hydraulic filament artificial muscles capable of in-situ sensitivity tunability is introduced. Using a neural network (NN) model, the new soft 3D sensor can precisely detect external forces based on the change of the hydraulic pressures with error of$\sim 1.0, \sim 1.3$, and$\sim 0.94$% in the$\text{x, y}$, and z-axis directions, respectively. The sensor is also able to sense large force ranges, comparable to other similar sensors available in the literature. The sensor is then integrated into a soft robotic surgical arm for monitoring the tool-tissue interaction during an ablation process.

Atrial fibrillation (AF) is mostly treated via robotic catheter-based cardiac ablation procedures. Over the last few decades, cables or tendon mechanisms are at the core of available cardiac catheters. Despite advances, the use of cables often results in considerable force loss, nonlinear hysteresis, and control challenges. Most catheters are not equipped with force sensing, which increases the risk of the ablation process and decreases their efficacy in clinical settings. In addition, current catheters have a poor user interface and therefore the ablation process requires skilled or trained surgeons to steer the complex motion of the catheter tip within the heart chambers. To improve the cardiac ablation procedure, a new robotic catheter that has the ability to extend its working space without moving its flexible body and a real-time force sensor for safe operation is highly desired. In this work, a new handheld and soft robotic catheter for AF ablation is introduced. The new device consists of several improved components such as a soft manipulator for navigation and bending motion, an ergonomic handheld controller, and a soft force sensor for monitoring tool-tissue contact. The design, modeling, and fabrication of the device are presented and followed by experimental characterizations and ex-vivo validation.

There is a growing need for vertical takeoff and landing vehicles, including drones, which are safe to use and can adapt to collisions. The risks of damage by collision, to humans, obstacles in the environment, and drones themselves, are significant. This has prompted a search into nature for a highly resilient structure that can inform a design of propellers to reduce those risks and enhance safety. Inspired by the flexibility and resilience of dragonfly wings, we propose a novel design for a biomimetic drone propeller called Tombo propeller. Here, we report on the design and fabrication process of this biomimetic propeller that can accommodate collisions and recover quickly, while maintaining sufficient thrust force to hover and fly. We describe the development of an aerodynamic model and experiments conducted to investigate performance characteristics for various configurations of the propeller morphology and related properties, such as generated thrust force, thrust force deviation, collision force, recovery time, lift-to-drag ratio, and noise. Finally, we design and showcase a control strategy for a drone equipped with Tombo propellers that collides in midair with an obstacle and recovers from collision continuing flying. The results show that the maximum collision force generated by the proposed Tombo propeller is less than two-thirds that of a traditional rigid propeller, which suggests the concrete possibility to employ deformable propellers for drones flying in a cluttered environment. This research can contribute to the morphological design of flying vehicles for agile and resilient performance.

Large-scale robotic skin with tactile sensing ability is emerging with the potential for use in close-contact human–robot systems. Although recent developments in vision-based tactile sensing and related learning methods are promising, they have been mostly designed for small-scale use, such as by fingers and hands, in manipulation tasks. Moreover, learning perception for such tactile devices demands a huge tactile dataset, which complicates the data collection process. To address this, this study introduces a multiphysics simulation pipeline, calledSimTacLS, which considers not only the mechanical properties of external physical contact but also the realistic rendering of tactile images in a simulation environment. The system utilizes the obtained simulation dataset, including virtual images and skin deformation, to train a tactile deep neural network to extract high-level tactile information. Moreover, we adopt a generative network to minimize sim2real inaccuracy, preserving the simulation-based tactile sensing performance. Last but not least, we showcase this sim2real sensing method for our large-scale tactile sensor (TacLink) by demonstrating its use in two trial cases, namely, whole-arm nonprehensile manipulation and intuitive motion guidance, using a custom-built tactile robot arm integrated with TacLink. This article opens new possibilities in the learning of transferable tactile-driven robotics tasks from virtual worlds to actual scenarios without compromising accuracy.

This paper presents a simple but compact design of a bicycle-like robot for inspecting complex-shaped ferromagnetic structures. The design concept for versatile locomotion relies on two independently steered magnetic wheels formed in a bicycle-like configuration, allowing the robot to possess multi-directional mobility. The key feature of a reciprocating mechanism enables the robot to change its shape when passing obstacles. A dynamic joint of the robot configuration makes it naturally adapt to uneven and complex surfaces of steel structures. We demonstrate the usability and practical deployment of the robot for steel thickness measurement using an ultrasonic sensor.

Electroadhesion is a versatile and controllable adhesion mechanism that has been used extensively in robotics. Soft electroadhesion embodies electrostatic adhesion in soft materials and is required for shape-adaptive and safe grasping of curved objects and delicate materials. In this work, we present a soft electroadhesive fabrication method based on laser scribing graphene oxide on a silicone film, which is cost-effective, facile and green. The method can be used to generate complex electroadhesive patterns without molds or stencils. We then present a 2D finite element model to demonstrate the shape-changing behavior and electric field distributions of a dual-mode parallel dielectric elastomer actuation and coplanar electroadhesion structure. The soft electroadhesive fabrication method based on laser-scribed graphene oxide electrodes and its experimental characterization results, together with its shape-morphing simulation model are expected to enable the wider adoption of soft electroadhesion in future robotics.

The sense of touch allows individuals to physically interact with and better perceive their environment. Touch is even more crucial for robots, as robots equipped with thorough tactile sensation can more safely interact with their surroundings, including humans. This article describes a recently developed large-scale tactile sensing system for a robotic link, called TacLINK, which can be assembled to form a whole-body tactile sensing robot arm. The proposed system is an elongated structure comprising a rigid transparent bone covered by continuous artificial soft skin. The soft skin of TacLINK not only provides tactile force feedback but can change its form and stiffness by inflation at low pressure. Upon contact with the surrounding environment, TacLINK perceives tactile information through the three-dimensional (3-D) deformation of its skin, resulting from the tracking of an array of markers on its inner wall by a stereo camera located at both ends of the transparent bone. A finite element model (FEM) was formulated to describe the relationship between applied forces and the displacements of markers, allowing detailed tactile information, including contact geometry and distribution of applied forces, to be derived simultaneously, regardless of the number of contacts. TacLINK is scalable in size, durable in operation, and low in cost, as well as being a high-performance system, that can be widely exploited in the design of robotic arms, prosthetic arms, and humanoid robots, etc. This article presents the design, modeling, calibration, implementation, and evaluation of the system.

Stable grip of wet, deformable objects is a challenging task for robotic grasping and manipulation, especially for food products' handling. The wet, slippery interfaces between the object and robotic fingers may require larger gripping force, resulting in higher risk of damaging the grasped object. This research aims to evaluate the role of micro-patterned soft pad on enhancement of wet adhesion in grasping a food sample in wet environment. We showcased this scenario with a tofu block 19.6×19.6×15mm3that is soft, and deformable object, gripped by a soft robotic gripper with two fingers. Each fingertip's surface, which directly makes contact with the tofu, was deposited soft pads in two cases: normal pads (flat surface) and a micropatterned pads. The micropatterned pad comprises of 14400 square cells, each cell has four 85 μm edges, surrounded by a channel network with 44 μm in depth. We conducted estimation of grasped force generated by pads in two cases, then verified by actual setup in griping the tofu block. Both estimated and experimental results reveal that the micropatterned pad decreased necessary load acting on the tofu's surface 2.2 times lower than that of the normal one, while maintaining the stability of the grasped tofu. The showcase in this paper supported the potential of micro patterns on soft fingertip in grasping deformable objects in wet environments without complicated control strategy, promising wider applications for robot in service section or food industry.

This paper presents a new approach for active tactile sensation that utilizes soft morphological computation. This work is inspired by human finger's wet-induced wrinkles, which appear after a long time soaking in water, and has been indicated as an efficient means for enhancement of gripping in wet environment. We created a tactile sensing system that is an integration of actuation (pneumatic actuator) and sensing elements (strain gauges). This device can change its morphology so that the posture of embedded sensing elements can vary, then generate different responses depending on the sensing tasks. As a result, this device can actively select its sensing functions depending on different sensing tasks. In this paper, the sensing device is both sensitive to indentation contact and sliding action on its surface by using only one type of strain gauge. This preliminary work is an example of soft morphological control in sensing, and expected to open a new trend in development of tactile sensing system.

This paper presents an analysis of the design and operation of a non-articulated robotic hand, inspired by the multilayered structure. The hand is made of a non-stretchable thin film, the two ends of which are attached to a soft substrate, forming an enclosed wrinkled shape. When the substrate is elongated, the morphology of the hand changes varied, resulting in a “wrapping” form around the grasped object. This design is applicable to a wide range of objects that can be grasped by the robotic hand. The smallest and biggest sizes of the grasped object are determined by the morphological computation of the film during design of the hand. This design also reduces the uncertainty of position between the hand and the object; i.e., stable grasping can be maintained as long as the object fits within the enclosed form of the hand. A mechanism controlling the morphology of the film using only one actuator, and for grasping food products such as fruits, is proposed. Preliminary results show the potential of the hand's morphology in the design of soft robotic mechanisms.

This paper describes an active tactile sensing system that selects sensing modalities based on specific sensing tasks, by changing its morphology, called Wrin'Tac. This paper was inspired by the human finger wet-induced wrinkle, which is usually observed when one soaks in warm water for a period, and has been indicated as an efficient transformation for enhancement of gripping stability in a wet environment. We proposed a device which is an integration of actuation (pneumatic actuator) and sensing elements (strain gauges) inside a thin, multilayered substrate. Under pressurization, the morphology of the substrate surface (both geometrical and mechanical characteristics) change with appearance of wrinkles. Especially, by formation of wrinkles, this device can change its shape so that the posture of embedded sensing elements (strain gauges) can vary and generate different responses depending on external load conditions. As a result, this device can actively select its sensing functions depending on specific sensing tasks. First, we created a model to investigate the dynamic changes in a strain gauges' mechanical response under formation of wrinkles. Then, a prototype of this sensing device and its fabrication process were proposed to accomplish sensing tasks under vertical indentation and horizontal sliding action on its surface by using one type of strain gauge. This paper is an example of soft morphological control in tactile sensing, and is expected to open a new avenue to development of tactile sensing systems.

Human-in-the-loop task involving soft contact has become common in robotic application, especially in physical human-robot interaction. In this task, it is required that robot would sense interactions with human by touching, as well as assess possibility of human approaching by proximity sensation. In addition, it is also essential to fabricate an interface so that human does not feel uncomfortable during physical interaction with robot. This paper presents an attempt on fabrication of sensing elements that can be utilized for construction of a soft interface (or a robotic skin). Each element is made from fabrics and soft materials that can sense both proximity and applied force from human's touch. In addition, each sensing element can sense the relative distance of conductive object (or human body) that is approaching the sensing element's surface, and the 2×2 contact force distribution when the object makes contact with the sensing element. By exploiting simultaneous measurement of capacitance, each fabric sensing element can smoothly switch the proximity mode and tactile mode based on position of the object. We also constructed a model that can predict variation of capacitance measurement of proximity and tactile modes during operation for further analysis. The methods and results presented in this paper can be extended to construct a larger scale of robotic skin for robot's body, and act as a platform for study human-robot interaction.

This paper focuses on sensing of McKibben actuator shrinkage using a fiber sensor. Control of McKibben actuators requires sensing their shrinkage without preventing their deformation. We applied electro-conductive yarn, with resistance related to its extensional strain, suggesting that measuring the resistance of the yarn can determine its length. The yarn is also bendable and light in weight, thus not preventing the deformation of a McKibben actuator. A fiber sensor consisting of these electro-conductive yarns was attached onto the membrane of a McKibben actuator to measure the distance between both ends of the sensor along the membrane. This paper describes a fiber sensor model that can calculate McKibben actuator shrinkage from the fiber sensor measurement. Based on the model, we investigated the arrangement of a fiber sensor so that the fiber extensional stress is within its allowable range. We then show a prototype of the fiber sensor that was utilized to measure McKibben actuator shrinkage. Experimentally, we found that the shrinkage of an actuator could be measured within 20 % error of its length.

We have proposed a Beam Bundle Model for modeling of a human fingertip during pushing and sliding action with friction, especially stick-to-slip transition, to overcome mentioned issues. In order to construct its three-dimensional non-homogeneous structure, we took sequence of magnetic resonant images, which bring consecutive cross-sectional layers of the human fingertip with distribution of skin, tissue, bone, and nail. Simulation results show a twofold aspect. Firstly, it can generate not only normal force distribution caused by pushing, but also response of friction force during sliding. Secondly, and more interestingly, the model dynamically produces localized displacement phenomenon on the contact area during stick-to-slip phase, which indicates how slippage erodes the contact area before the total slippage of the fingertip occurs. Finally, we investigated role of sliding mechanism acting on human fingertips' contact area in stable lifting of an object, in order to show the potential of the model in studying tactile mechanism of human and apply to robotic systems.

Humans usually distinguish objects by sliding their fingertips on the surface to feel the texture via mechanoreceptor underneath the skin. We have developed a human-imitated system for robotic fingertip to sense object's texture via sliding action. Design of the sensory skin was inspired by the localized displacement phenomenon of a sliding soft fingertip ([1]) to capture stick-slip events on the contact surface that mainly represent texture characteristics. The soft skin is knitted by electro-conductive tension-sensitive yarns, then covered over a hemispherical fingertip. The pile-shaped surface of the fabric sensor enhances tangential traction detection ability of the sensor, even though the normal load is also sensible. Our aim is to exploit this sensor in applications regarding relative sliding between the touched object and the surface of the sensor, such as slip detection ([2]), and surface identification in this paper. In surface encoding, we have experimentally investigated ability of the fabric sensor in recognition touched objects via multiple machine learning algorithms, such as naive Bayes, Multi-Layer Artificial Neural Network (ANN) with input extracted from autoregressive models, and ANN with input extracted from Discrete Wavelet Transformation (DWT), have been trained to distinguish three typical textures. As a result, we have found that the last method outperforms the remains with an average successful rate of 90%.

We have proposed a dynamic model to investigate the sliding motion of a 3-dimensional soft fingertip on a plane with friction. The fingertip is comprised virtually of a finite number of elastic compressible and bendable cantilevers whose free ends act as infinitesimal contact points. The contact surface is afterward meshed using finite element method based on coordinates of contact points. By introducing Coulomb's law and contact compliance into each contact point, we are able to assess the frictional characteristic during sliding motions of the fingertip. We also successfully described dynamically localized displacements on the contact surface during stick slip transition, which occurs substantially on hemispherical soft fingertip. Moreover, each simulation trial was implemented in a very small amount of time comparing to commercial softwares, promising use of this model in realtime application In addition, we conducted experiments to valid proposed simulation, including force/moment and vision setups.

We have developed a slip sensor which is knitted by tension-sensitive electro-conductive yarns. When elongating this yarn, its resistance will drop remarkably. Because the yarn is mainly sensitive to deformation along its main axis, a special way to knit these yarns has been proposed to form a slip sensor. This sensor is used in detection of the human fingertip's slip during rubbing action on its surface. We found that, a simple derivative of the sensor's output was sufficient to detect slippage. However, in some cases, the sensor gets troublesome to distinguish between change of normal load and the occurrence of slip, since human implements their action without caring much about keeping the stable applied force on the sensor. Therefore, a well-known DWT (Discrete Wavelet Transform) method is employed to overcome this problem. As a result, depending on the purpose of the application, several data processing methods are employed to detect slippage of human's rubbing action, or robotic fingertip. Results in this paper promise an applicable sensory mean, which can be employed in haptic devices, teleoperation, or robotic skin.

We describe the development of a tactile hemispherical soft fingertip (FT) of a size similar to that of a human thumb. The sensory core consists of a microscaled force/torque sensor that can output one component of force and two components of moment simultaneously, which was developed beforehand. This sensor is embedded in a polyurethane rubber hemispherical dome to form a complete soft, compliant, and perceptible robotic FT. This system is designed for easy fabrication, high reliability in outputting signals, and stable operation. Static and dynamic mathematical analyses were utilized to investigate the responses of the sensor during the typical sliding motion of an FT. This was followed by experiments to show its potential in tactile and texture recognition. Especially, incipient-slip detection, which is critical in grasping manipulations, can be assessed properly and in a timely way. The development of this tactile FT is considered significant in the field of dexterous manipulation.

This paper describes our initial research on development of a tactile sensor, which can be employed in anthropomorphic soft fingertips, with multi-sensing points that uses 3-DOF micro force moment sensing chips (MFMS) which are able to measure forces up to nearly a Newton. Three sensing points are integrated on a compact printed circuit board with an in-built multiplexer circuit for the purpose of saving energy and reducing the number of outputs. This system was designed for the purpose of manipulating small-scaled objects, and realizing special characteristics of the objects such as distribution of edges/borders. The process including design, fabrication, and calibration will be explained in detail in this paper.

We describe here our proposed method to investigate the sliding motion of a soft fingertip in the dexterous manipulation. This paper focuses on analyzing dynamic sliding motion of a 2-dimensional (2-D) soft fingertip on a plane. To investigate the deformation of the fingertip during this process, we consider the soft fingertip as if it was composed of a finite number of elastic cantilevers which are compressible and bendable. Simulation will be carried out firstly on this 2-D model of soft fingertip, focusing on the analysis of incident slip-page. After that, various experimental results will be shown to verify this model.

We have investigated the deformation of a soft fingertip when it slides. This process was first simulated using the non-linear Finite Element Analysis (FEA) method. Based on the results of this simulation, we designed experiments to observe the sliding and object grasping of a soft fingertip, in which a 3-DOF (degree of freedom) micro force/moment sensor was embedded. With this sensor, forces and moments acting in the fingertip are measured based on the piezoresistive effect. These measurements provide information on the status of contact and sliding of a soft fingertip on a surface. Based on these results, incipient slip, which has an important role in object gripping by a robot manipulator, can be realized. Textile's texture recognition experiments were also conducted to assess potentials of the fingertip in tactile and texture perception.

We have investigated the deformation of a soft fingertip when it slides. This process was first simulated using ANSYS software with non-linear Finite Element Analysis (FEA). Based on the results of this simulation, we designed experiments to observe the sliding and object grasping of a soft fingertip, in which, a 4-DOF (degree of freedom) micro force/moment sensor was embedded inside the soft fingertip. With this sensor, values of force and moment acting in the fingertip are measured, based on the piezoresistive effect. These measurements provide information on the status of contact and sliding of a soft fingertip on a surface. Based on these results, incipient slip, which has an important role in object gripping by a robot manipulator, can be realized.