Stochastic resonance (SR) is a phenomenon occurring in some nonlinear systems by which a signal provided as input that is too small in magnitude to normally influence the system's output can actually influence the system's output once a non-zero level of noise is provided. SR has been extensively studied both theoretically and experimentally, and noise has been exploited for improving the performance in both biological and artificial systems. In addition to its scientific importance, the use of noise has attracted interest because of its potential to overcome the present limitations in engineering applications. In this study, we investigate the use of a universal approximator exploiting SR as a new realization of well-established feed-forward neural networks. The proposed universal approximator consists of groups of threshold elements. Although the approximation universality of a network consisting of threshold elements has been proven in terms of extreme learning machine implementations, once SR is taken into account, the system can be modeled in a form identical to that of a classic generic three-layered neural network, for which the universal approximation capability has been proven. The capability of the proposed approximator for serving as a universal approximator is first proven theoretically in the limit of an infinite number of hidden units. Subsequently, the performance achieved by the backpropagation type and the extreme learning machine type learning algorithms is experimentally evaluated for cases involving limited numbers of hidden units, highlighting the SR effect occurring in the proposed system.

The sit-to-stand movement is an apparently simple yet fundamental activity of daily life. Failure of performing this movement strongly impacts the quality of life of an increasing number of elderly people. Much research thus focuses on assisting this motion. The time necessary for rising up from a sit position is very short for healthy subjects, in the order of few seconds. Using similar speeds for the assisted motion would create safety concerns, hence slower speeds are usually employed. In this paper we experimentally investigate the effects of this speed reduction. We detail the relationship among robot's speed, forces acting on the robot's user and muscular activation. From the results of this analysis we derive indications on the speeds appropriate for assisting the sit-to-stand movement.

This paper presents ROSlink, an open source project that aims at easing the integration of legacy systems with ROS (Robot Operating System). Its design principles provide a set of unique features that make it appealing for the interconnection of ROS with systems where ROS itself cannot be installed. First, ROSlink requires very limited changes to the legacy system. The project is self contained, bringing in no dependencies, which may be difficult to satisfy in a legacy system. Furthermore, with ROSlink any data type already in use in the legacy system can be employed for the communication of topics and service requests and responses. ROSlink allows run-time rerouting of the communication between the legacy system and ROS. Moreover, it empowers the legacy code with the ROS name remapping system, without enforcing any constraint on the command line parameters of legacy programs. Finally, by simply using a set of API that closely follow the ROS programming interface, ROSlink simplifies any successive porting of the code to a real ROS system. In this paper, the main design choices of ROSlink are discussed. A list of practical applications and tests where ROSlink was employed, as well as a short discussion on the project's future directions are then given.

Touch is an important means for communication among humans. Sport instructors or dance teachers often use touch to adjust students' postures in a very intuitive way. Using tactile instructions appears thus to be a very appealing modality for developing humanoid robot motions as well. Spontaneous interpretation of tactile instructions given by users reveals itself to be a complex task for artificial systems. This paper describes a proof of concept system for robot motion creation based on tactile interaction. The system is interesting for two reasons. Firstly, it shows the feasibility of using tactile instructions for motion development. Secondly, it can be used as a tool for studying the way humans intuitively use touch to communicate. This, in turn, will allow the development of better algorithms for predicting the meaning of tactile instructions. Results of a pilot experiment are discussed, and a first set of features of tactile communication, yielded by the analysis of the data collected, is identified.