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Compliance has been exploited in various forms in robotic systems to allow rigid mechanisms to come into contact with fragile objects, or with complex shapes that cannot be accurately modeled. Force feedback control has been the classical approach for providing compliance in robotic systems. However, by integrating other forms of instrumentation with compliance into a single device, it is possible to extend close monitoring of nearby objects before and after contact occurs. As a result, safer and smoother robot control can be achieved both while approaching and while touching surfaces. This paper presents the design and extensive experimental evaluation of a versatile, lightweight, and low-cost instrumented compliant wrist mechanism which can be mounted on any rigid robotic manipulator in order to introduce a layer of compliance while providing the controller with extra sensing signals during close interaction with an object's surface. Arrays of embedded range sensors provide real-time measurements on the position and orientation of surfaces, either located in proximity or in contact with the robot's end-effector, which permits close guidance of its operation. Calibration procedures are formulated to overcome inter-sensor variability and achieve the highest available resolution. A versatile solution is created by embedding all signal processing, while wireless transmission connects the device to any industrial robot's controller to support path control. Experimental work demonstrates the device's physical compliance as well as the stability and accuracy of the device outputs. Primary applications of the proposed instrumented compliant wrist include smooth surface following in manufacturing, inspection, and safe human-robot interaction. [ABSTRACT FROM AUTHOR]

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture. [ABSTRACT FROM AUTHOR]