Neurophysiology of slip sensation and grip reaction: insights for hand prosthesis control of slippage

Sensory feedback is pivotal for a proficient dexterity of the hand. By modulating the grip force in function of the quick and not completely predictable change of the load force, grabbed objects are prevented to slip from the hand. Slippage control is an enabling achievement to all manipulation abilities. However, in hand prosthetics, the performance of even the most innovative research solutions proposed so far to control slippage remain distant from the human physiology. Indeed, slippage control involves parallel and compensatory activation of multiple mechanoceptors, spinal and supraspinal reflexes, and higher-order voluntary behavioral adjustments. In this work, we reviewed the literature on physiological correlates of slippage to propose a three-phases model for the slip sensation and reaction. Furthermore, we discuss the main strategies employed so far in the research studies that tried to restore slippage control in amputees. In the light of the proposed three-phase slippage model and from the weaknesses of already implemented solutions, we proposed several physiology-inspired solutions for slippage control to be implemented in the future hand prostheses. Understanding the physiological basis of slip detection and perception and implementing them in novel hand feedback system would make prosthesis manipulation more efficient and would boost its perceived naturalness, fostering the sense of agency for the hand movements.

Electronic skins and machine learning for intelligent soft robots

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Grip Stabilization of Novel Objects Using Slip Prediction

Controlling contact with arbitrary, unknown objects defines a fundamental problem for robotic grasping and in-hand manipulation. In real-world scenarios, where robots interact with a variety of objects, the sheer number of possible contact interactions prohibits acquisition of the necessary models for all objects of interest. As an alternative to traditional control approaches that require accurate models, predicting the onset of slip can enable controlling contact interactions without explicit model knowledge. In this article, we propose a grip stabilization approach for novel objects based on slip prediction. Using tactile information, such as applied pressure and fingertip deformation, our approach predicts the emergence of slip and modulates the contact forces accordingly. We formulate a supervised-learning problem to predict the future occurrence of slip from high-dimensional tactile information provided by a BioTac sensor. This slip mapping generalizes across objects, including objects absent during training. We evaluate how different input features, slip prediction time horizons, and available tactile information channels, impact prediction accuracy. By mounting the sensor on a PA-10 robotic arm, we show that employing prediction in a controller's feedback loop yields an object grip stabilization controller that can successfully stabilize multiple, previously unknown objects by counteracting slip events.

An eight-legged tactile sensor to estimate coefficient of static friction

It is well known that a tangential force larger than the maximum static friction force is required to initiate the sliding motion between two objects, which is governed by a material constant called the coefficient of static friction. Therefore, knowing the coefficient of static friction is of great importance for robot grippers which wish to maintain a stable and precise grip on an object during various manipulation tasks. Importantly, it is most useful if grippers can estimate the coefficient of static friction without having to explicitly explore the object first, such as lifting the object and reducing the grip force until it slips. A novel eight-legged sensor, based on simplified theoretical principles of friction is presented here to estimate the coefficient of static friction between a planar surface and the prototype sensor. Each of the sensor's eight legs are straight and rigid, and oriented at a specified angle with respect to the vertical, allowing it to estimate one of five ranges (5 = 8/2 + 1) that the coefficient of static friction can occupy. The coefficient of friction can be estimated by determining whether the legs have slipped or not when pressed against a surface. The coefficients of static friction between the sensor and five different materials were estimated and compared to a measurement from traditional methods. A least-squares linear fit of the sensor estimated coefficient showed good correlation with the reference coefficient with a gradient close to one and an r(2) value greater than 0.9.

Experimental Discussion of Occurrence of High-Frequency Component on Slip Sensor Output Using Pressure Conductive Rubber

We have presented a slip sensor that uses pressureconductive rubber to detect initial slip, but have not revealed the principle of high-frequency wave occurrence that is used by this detection. The wave-occurrence principle should be clarified in optimized slip sensor design, especially the properties of pressure-conductive rubber and the detector shape and for reducing individual differences in detection characteristics of the slip sensor. This paper discusses the wave-occurrence principle through a series of experiments and shows that localized fixing and peeling between pressure-conductive rubber and electrodes in the slip sensor configuration have important relation to the principle.

Neural Network Training Techniques for Enhancing the Performance of a Distributive Tactile Sensor

This work describes neural network training techniques for enhancing the performance of a one-dimensional beam for determining load positions. The system was a distributive system which relied on the detection of changes in the surface properties that can be seen across the surface. The demonstrated distributive system was 400 mm in length. The applied load positions within the range 60–300 mm could be determined with an average percentage error of 0.2% of the beam length which corresponded to a position error of 0.8 mm using a network trained with 10 training positions. It was found that the errors were higher for the load applied near the edges of the beam, leading to an average percentage error of 3.6% for the whole length of the beam. The normalization of the network output can be employed to reduce the average percentage error by approximately 1% for a given number of training positions. The performance was improved by introducing more training positions in the less sensitive area. The described training technique not only reduced the prediction error but also enlarged the areas where the prediction errors were satisfactorily small. Using the described technique, the overall prediction error could be decreased by 0.13–0.24% of the beam length for 30–10 training positions.

The area where the prediction errors were within 2 mm was increased by 14.5–7.3% of the beam length for a network trained with 4 training positions in the middle portion using 10–30 training positions.

High Speed and High Sensitivity Slip Sensor for Dexterous Grasping

Slip-detecting tactile sensors are essential if robot hands are ever to achieve the gripping motions of human hands. In our previous research, we developed a flexible, thin, and lightweight slip sensor that exploits resistance changes in pressure conductive rubber. However, using this sensor, it was difficult to distinguish between object slip and changes in normal force. Therefore, in this research, we investigate a method of identifying object slip by analyzing the frequency components of the output signal from the sensor. As a result, we find that high-frequency components of several kilohertz or more are included in the complex voltage signal immediately before object slip. Therefore, using this high-frequency component, we develop a simple structure sensor that distinguishes between both contact and a state of immediately before slip with high sensitivity. Moreover, we design a slip sensor for a robot hand and examine the effects of noise by manipulation. Finally, we describe an experiment involving the adjustment of the gripping force of a robot hand.

Contact State Estimation by Vision-Based Tactile Sensors for Dexterous Manipulation with Robot Hands Based on Shape-Sensing

We propose a new method for estimating the contact state of objects with varying shapes on a vision-based fluid-type tactile sensor, which touch pad is an elastic transparent membrane of silicon rubber with dotted pattern printed on its inner side. The membrane is filled with translucent red colored water. The proposed method leads to better understanding of the object's shape and movement, and can be applied for accomplishing reliable and dexterous handling tasks by robot hands.

Bio-inspired grasp control in a robotic hand with massive sensorial input

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.