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We present our second generation tactile sensor for the Shadow Dexterous Hand's palm. We were able to significantly improve the tactile sensor characteristics by utilizing our latest barometer-based tactile sensing technology with linear (R² ≥ 0.9996) sensor output and no noticeable hysteresis. The sensitivity threshold of the tactile cells and the spatial density were both dramatically increased. We demonstrate the benefits of the new sensor by re-running an experiment to estimate the stiffness of different objects that we originally used to test our first generation palm sensor. The results underline a considerable performance boost in estimation accuracy, just due to the improved tactile skin. We also propose a revised neural network architecture that even further improves the average classification accuracy to 96% in a 5-fold cross-validation.

Our fingernails help us to accomplish a variety of manual tasks, but surprisingly only a few robotic hands are equipped with nails. In this paper, we present a sensorized fingernail for mechatronic hands that can capture static and dynamic interaction forces with the nail. Over the course of several iterations, we have developed a very compact working prototype that fits together with our previously developed multi-cell tactile fingertip sensor into the cavity of the distal phalange of a human-sized robotic hand. We present the construction details, list the key performance characteristics and demonstrate an example application of finding the end of an adhesive tape roll using the signals captured by the sensors integrated in the nail. We conclude with a discussion about improvement ideas for future versions.

The ability to discriminate between target and distractors, using the information perceived by the hand over time, is essential to perform haptic search successfully, be it for a human hand or a suitably sensorized anthropomorphic robot hand. To address the latter, we train a binary classifier to perform this discrimination during unconstrained haptic search performed by sighted study participants who were blindfolded. In this work, we test different representational concepts and compare the results with the human classification performance. This approach both guides our understanding of human haptic interaction with the 3D environment and aids future modeling of artificial touch for anthropomorphic robot hands. Our contribution is three-fold. Firstly, we are able to acquire a synchronized multimodal time series of exceptionally high spatio-temporal resolution of both the 3D environment and the hand with our novel experimental setup. It includes our Modular Haptic Stimulus Board to represent a 3D environment and a novel tactile glove equipped with position tracking markers and joint angle sensors. Secondly, we introduce a machine learning approach inspired by a novel application of the feature guidance concept for vision (Wolfe et al., 2007 [1]) to modeling of haptic search in a 3D environment, focusing on the target-distractor discrimination. Finally, we compare results for two different types of artificial neural networks, a feedforward and a recurrent network. We show that using recurrent networks, and therefore integrating information over time, improves the classification results. The evaluation also shows that classification accuracy is the highest for the combination of both the tactile and the joint angle modalities.

The advent of advanced tactile sensing technology triggered the development of methods to employ them for grasp evaluation, online slip detection, and tactile servoing. In contrast to recent approaches to slip detection, distinguishing slip from non-slip conditions, we consider the more difficult task of distinguishing different types of slippage. Particularly we consider an object pushing task, where forces can only be applied from the top. In that case, the robot needs to notice when the object successfully moves vs. when the object gets stuck while the finger slips over its surface. As an example, consider the task of pushing around a piece of paper. We propose and evaluate three different convolutional network architectures and proof the applicability of the method for online classification in a robot pushing task.

We present a novel, soft, tactile skin composed of a fabric-based, stretchable sensor technology based on the piezoresistive effect. Softness is achieved by a combination of a soft silicone padding covered by a skin of more durable, tearproof silicone with an imprinted surface pattern mimicking human glabrous skin, found e.g. in fingertips. Its very thin layer structure (starting from 2.5 mm) facilitates integration on existing robot surfaces, particularly on small and highly curved links. For example, we augmented our Shadow Dexterous Hand with 12 palm sensors, and 2 resp. 3 sensors in the middle resp. proximal phalanges of each finger. To demonstrate the usefulness and efficiency of the proposed sensor skin, we performed a challenging classification task distinguishing squeezed objects based on their varying stiffness.

In the process of developing human-like robotic hands, engineers are looking for robust and accurate sensors that can fit in very confined spaces. Rotary encoders that measure joint angles of a multi-fingered hand must be of very small size and yet provide reliable and repetitive data. Hall-effect sensors combined with ring magnets have proved to be efficient and durable rotary magnetic position sensors mainly due to their contact-less features. However, when several sensors of this kind are packed closely together in a finger, magnetic crosstalk effects appear and can lead to important errors/shifts in nearby sensor readings. This paper describes magnetic crosstalk effects in nearby joints and proposes a compensation method directly linked with the software calibration process.

This paper presents a method for the automatic observation of unknown objects. We aim at finding the position of the targeted object and at capturing multiple views of its shape using a single eye-in-hand camera. The main goal is modeling the unknown object via 3D reconstruction before grasping and manipulating it with a robot hand. The proposed method built over a visual servo loop uses simple features to constantly gaze at the object and handles a three-step exploration with the same control structure. Images captured with the experimental setup are processed online to recover a model with millimetric accuracy.

Proceedings of: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO 2009) December 19-23, 2009, Guilin, China This paper presents a complete 3D-reconstruction method optimized for online object modeling in the context of object grasping by a robot hand. The proposed solution is based on images captured by an eye-in-hand camera mounted on the robot arm and is an original combination of classical but simplified reconstruction methods. The different techniques used form a process that offers fast, progressive and reactive reconstruction of the object. European Community's Seventh Framework Program The research leading to these results has been partially supported by the HANDLE project, which has received funding from the European Communitity’s Seventh Framework Programme (FP7/2007-2013) under grant agreement ICT 231640