deForm: An interactive malleable surface for capturing 2.5D arbitrary objects, tools and touch

We introduce a novel input device, deForm, that supports 2.5D touch gestures, tangible tools, and arbitrary objects through real-time structured light scanning of a malleable surface of interaction. DeForm captures high-resolution surface deformations and 2D grey-scale textures of a gel surface through a three-phase structured light 3D scanner. This technique can be combined with IR projection to allow for invisible capture, providing the opportunity for co-located visual feedback on the deformable surface. We describe methods for tracking fingers, whole hand gestures, and arbitrary tangible tools. We outline a method for physically encoding fiducial marker information in the height map of tangible tools. In addition, we describe a novel method for distinguishing between human touch and tangible tools, through capacitive sensing on top of the input surface. Finally we motivate our device through a number of sample applications

HaptiTemp: A Next-Generation Thermosensitive GelSight-like Visuotactile Sensor

This study describes the creation of a new type of compact skin-like silicone-based thermosensitive visuotactile sensor based on GelSight technology. The easy integration of this novel sensor into a complex visuotactile system capable of very rapid detection of temperature change (30°C/s) is unique in providing a system that parallels the withdrawal reflex of the human autonomic system to extreme heat. To the best of authors’ awareness, this is the first time a sensor that can trigger a sensory impulse like a withdrawal reflex of humans in robotic community. To attain this, we used thermochromic pigments color blue, orange, and black with a threshold of 31°C, 43°C, and 50°C, respectively on the gel material. Each pigment has the property of becoming translucent when its temperature threshold is reached, making it possible to stack thermochromic pigments of different colors and thresholds. The pigments were air-brushed on a low-cost commercially available transparent silicone sponge. We used MobileNetV2 and transfer learning to simulate tactile preprocessing in order to recognize five different objects. The new thermosensitive visuotactile sensor helped to achieve 97.3% tactile image classification accuracy of five different objects. Our novel thermosensitive visuotactile sensor could be of benefit in material texture analysis, telerobotics, space exploration, and medical applications

Rapid manufacturing of color-based hemispherical soft tactile fingertips

Tactile sensing can provide access to information about the contact (i.e. slippage, surface feature, friction), which is out of reach of vision but crucial for manipulation. To access this information, a dense measurement of the deformation of soft fingertips is necessary. Recently, tactile sensors that rely on a camera looking at a deformable membrane have demonstrated that a dense measurement of the contact is possible. However, their manufacturing can be time-consuming and labor-intensive. Here, we show a new design method that uses multi-color additive manufacturing and silicone casting to efficiently manufacture soft marker-based tactile sensors that are able to capture with high-resolution the three-dimensional deformation field at the interface. Each marker is composed of two superimposed color filters. The subtractive color mixing encodes the normal deformation of the membrane, and the lateral deformation is found by centroid detection. With this manufacturing method, we can reach a density of 400 markers on a 21 mm radius hemisphere, allowing for regular and dense measurement of the deformation. We calibrated and validated the approach by finding the curvature of objects with a threefold increase in accuracy as compared to previous implementations. The results demonstrate a simple yet effective approach to manufacturing artificial fingertips for capturing a rich image of the tactile interaction at the location of contact

Visuotactile Sensors with Emphasis on GelSight Sensor: A Review

This review paper focuses on vision and touch-based sensors known as visuotactile. The study of visuotactile sensation and perception became a multidisciplinary field of study by philosophers, psychologists, biologists, engineers, technologists, and roboticists in the fields of haptics, machine vision, and artificial intelligence and it dates back centuries. To the best of our knowledge, the earliest records of visuotactile sensor was not applied to robotics and was not even for hand or finger imprint analysis yet for recording the foot pressure distribution of a walking or standing human known as pedobarograph. Our review paper presents the different literature related to visuotactile sensors that lead to a high-resolution miniature pedobarographlike sensor known as the GelSight sensor. Moreover, this review paper focuses on architecture, different techniques, hardware, and software development of GelSight sensor since 2009 with its applications in haptics, robotics, and computer vision