The two major components of a robotic tactile-sensing system are the tactile-sensing hardware at the lower level and the computational/software tools at the higher level. Focusing on the latter, this research assesses the suitability of computational-intelligence (CI) tools for tactile-data processing. In this context, this paper addresses the classification of sensed object material from the recorded raw tactile data. For this purpose, three CI paradigms, namely, the support-vector machine (SVM), regularized least square (RLS), and regularized extreme learning machine (RELM), have been employed, and their performance is compared for the said task. The comparative analysis shows that SVM provides the best tradeoff between classification accuracy and computational complexity of the classification algorithm. Experimental results indicate that the CI tools are effective in dealing with the challenging problem of material classification.

In the past few years a new scenario for robot-based applications has emerged. Service and mobile robots have opened new market niches. Also, new frameworks for shop-floor robot applications have been developed. In all these contexts, robots are requested to perform tasks within open-ended conditions, possibly dynamically varying. These new requirements ask also for a change of paradigm in the design of robots: on-line and safe feedback motion control becomes the core of modern robot systems. Future robots will learn autonomously, interact safely and possess qualities like self-maintenance. Attaining these features would have been relatively easy if a complete model of the environment was available, and if the robot actuators could execute motion commands perfectly relative to this model. Unfortunately, a complete world model is not available and robots have to plan and execute the tasks in the presence of environmental uncertainties which makes sensing an important component of new generation robots. For this reason, today's new generation robots are equipped with more and more sensing components, and consequently they are ready to actively deal with the high complexity of the real world. Complex sensorimotor tasks such as exploration require coordination between the motor system and the sensory feedback. For robot control purposes, sensory feedback should be adequately organized in terms of relevant features and the associated data representation. In this paper, we propose an overall functional picture linking sensing to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic qualities of haptic perception in humans inspire the models and categories comprising the proposed classification. The objective is to provide a reasoned, principled perspective on the connections between different taxonomies used in the Robotics and human haptic literature. The specific case of active exploration is chosen to ground interesting use cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has been treated only to a limited extent compared to grasping and manipulation. Second, exploration involves specific robot behaviors that exploit distributed and heterogeneous sensory data.

The availability of compact fast circuitry for the support of artificial neural systems is a long-standing and critical requirement for many important applications. This brief addresses the implementation of the powerful extreme learning machine (ELM) model on reconfigurable digital hardware (HW). The design strategy first provides a training procedure for ELMs, which effectively trades off prediction accuracy and network complexity. This, in turn, facilitates the optimization of HW resources. Finally, this brief describes and analyzes two implementation approaches: one involving field-programmable gate array devices and one embedding low-cost low-performance devices such as complex programmable logic devices. Experimental results show that, in both cases, the design approach yields efficient digital architectures with satisfactory performances and limited costs.

Reduced-reference systems can predict in real-time the perceived quality of images for digital broadcasting, only requiring that a limited set of features, extracted from the original undistorted signals, is transmitted together with the image data. This paper uses descriptors based on the color correlogram, analyzing the alterations in the color distribution of an image as a consequence of the occurrence of distortions, for the reduced reference data. The processing architecture relies on a double layer at the receiver end. The first layer identifies the kind of distortion that may affect the received signal. The second layer deploys a dedicated prediction module for each type of distortion; every predictor yields an objective quality score, thus completing the estimation process. Computational-intelligence models are used extensively to support both layers with empirical training. The double-layer architecture implements a general purpose image quality assessment system, not being tied up to specific distortions and, at the same time, it allows us to benefit from the accuracy of specific, distortion-targeted metrics. Experimental results based on subjective quality data confirm the general validity of the approach.