Tactile sensing is one important element that can enable robots to interact with an unstructured world. By having tactile perception, a robot can explore its environment by touching objects. Like human skin, a tactile sensor that can provide rich information such as distributed normal and shear forces with high density can help the robot to recognize objects. In previous work we introduced uSkin, a soft skin with distributed 3-axis force sensitive elements, and a center-to-center distance between the 3-axis load cells of 4.7 mm for the flat version. The current paper presents a new structure for the distributed soft force transducer which reduces the crosstalk between the components of the 3-axis force measurements. 3D printing the silicon structure eased the prototype production. However, the 3D printed material has a higher hysteresis than the previously used Ecoflex. Microcontroller boards originally developed for the skin of iCub were implemented for uSkin, increasing the readout frequency and reducing the space requirements and number of wires. The sensor was installed on iCub and successfully used for shape exploration.

This paper presents an easy means to produce a 3-axis Hall effect–based skin sensor for robotic applications. It uses an off-the-shelf chip and is physically small and provides digital output. Furthermore, the sensor has a soft exterior for safe interactions with the environment; in particular it uses soft silicone with about an 8 mm thickness. Tests were performed to evaluate the drift due to temperature changes, and a compensation using the integral temperature sensor was implemented. Furthermore, the hysteresis and the crosstalk between the 3-axis measurements were evaluated. The sensor is able to detect minimal forces of about 1 gf. The sensor was calibrated and results with total forces up to 1450 gf in the normal and tangential directions of the sensor are presented. The test revealed that the sensor is able to measure the different components of the force vector.

This paper introduces a novel tri-axial capacitive force sensor. The sensor can measure the force vector, is embedded in soft 7 mm-thick silicone skin, enables temperature sensitivity compensation and has digital output. To measure the force vector, tilted capacitive sensor elements are used which are facing in different directions to differentiate the tangential forces. The sensor is intended for distributed contact sensing in a robotic skin, but could be also used for other applications such as novel haptic user interfaces in wearable devices. A series of experiments was performed and showed good sensor characteristics. The concept of the tilted force transducers has been proven to have the capability of detecting the force vector acting on the local sensor surface. Graphical Abstract

Recently, tactile sensing has attracted increasing attention for robotic manipulation. Predicting the grasping stability before lifting objects and detecting the ongoing/onset of slip after lifting objects are two critical and widely studied tasks in robotic tactile manipulation. Previous methods focus on proposing novel neural networks (NN) architectures towards one of the above two tasks and did not consider that the two tasks are employed in two interconnected action-phases, i.e. grasping and lifting. Therefore, we firstly explore the possibility of constructing a multi-phase, multi-output framework to combine the stability prediction before lifting and the slip detection after lifting. Moreover, to improve the prediction/detection accuracy, we also proposed to explicitly ensemble different NN architectures using various methods, including attention mechanisms. Our experiments are done with 6 state-of-art NN architectures on two datasets including more than 3000 robotic grasps over 80 objects in total. Furthermore our proposals are tested in a real-time robot experiment with unseen objects. Our experimental results show that the proposed multi-phase, multi-output model exhibits more reliable and flexible performance than a single phase model. We also show that using the ensemble of different NN architectures is a viable and practical choice to boost the overall performance.

Human training data can scaffold robot imitation learning. However, most datagloves only record joint angles, but for many tasks, force control is more important. They also cover the human skin, thereby making a natural interaction of the human with the object impossible. This letter suggests a wearable sensor that can measure the three-axis force vector exerted at the fingertip, without covering the skin that contacts the object. Using two small-sized Hall effect based three-axis sensors mounted on the sides of the fingertips, the finger pad deformation resulting from the force vector acting on the fingertip is measured. Experiments with ten subjects show that the force vectors can be measured with reasonable precision. Furthermore, the influence of the Earth's magnetic field and the finger's orientation on the finger tactile sensor's measurement has been reduced from ±4N to ±1N .

This paper presents major improvements to a multimodal, adjustable sensitivity skin sensor module. It employs a geomagnetic 3-axis Hall effect sensor to measure changes in the position of a magnetic field generated by an electromagnet. The electromagnet is mounted on a flexible material, and different current values can be supplied to it, enabling adjustments to the sensitivity of the sensor during operation. Capacitive sensing has been added in this iteration of the module, with two sensing modalities: “pre-touch” detection with proximity sensing and normal force capacitive sensing. The sensor has been designed to be interconnected with other sensor modules to be able to cover large surfaces of a robot with normal and shear force sensing and object proximity detection. Furthermore, this paper introduces important size reductions of the previous sensor design, calibration results, and further analysis of other sensor characteristics.