A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification was realized.In this context, the nonstationarity of EEG signals is a critical issue and can lead to the Dataset Shift problem.Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods.418 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment.Among these papers, 75 were found eligible based on their relevance to the problem.Studies lacking a specific cross-subject and cross-session validation strategy and making use of other biosignals as support were excluded.On the basis of the selected papers' analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion on the different ML approaches involved.The studies with the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches.A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.

In this paper we present a novel approach to human-robot control. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, named KURE in this paper, with the ability to adapt both towards the user and the robotic morphology. The aim is for a transparent mechanism connecting user and robot, allowing for a seamless integration of control signals and robot behaviours. Starting from a tabula rasa basis, KURE is able to identify control patterns (behaviours) for the given robotic morphology and successfully merge them with control signals from the user, regardless of the input device used. The structural components of the interface are presented and assessed both individually and as a whole.

This paper investigates time-delay effects of the human social interaction to understand how human can adapt to the time delay, which will be required in software agents to establish a harmonic interaction with human. We performed the minimal experiments of social interaction called perceptual crossing experiments with time delay. Our result shows that the social interaction breaks down when the total amount of time delay is given more than about one second. However, the interaction breaks more easily when the time delay is given to both participants than to either participant.

Cellular Automata represent a formal frame for dynamical systems which evolve on the base of local interactions. We here present first results of the CUDA parallelization of the SCIDDICA S3-hex Complex Cellular Automata model for simulating debris flows. In particular, a first strategy for the parallelization of the model is based on a straightforward one thread - one cell approach, where each cell in the cellular space is computed by a CUDA thread. A second approach concerns the adoption of a list of CA computational active cells which is handled step by step by an efficient stream compaction algorithm, in order to reduce the excessive use of computationally inactive threads. First results performed on different graphic processors have shown that, by adopting the different CUDA strategies, this kind of hardware can be effective for landslide risk mitigation.

In this paper we present a novel idea for the creation of an intelligent interface that allows the remote control of arbitrarily complex robotics morphologies by translating intuitive human behaviours into purposeful robotic actions. By taking inspiration from human robot interaction, ergonomic principles, and autonomous robotics this paper proposes a human-centric framework for robot control inspired by the current advancements in recurrent neural networks and self-organisation. In particular, we present an integrated approach based on neural networks for input acquisition from human operator and self organisation for the acquisition of robot behaviours. We realise the interface as a kind of intelligent agent connecting the two end points of the system: Human and robot, providing an adaptive and intelligent interface for robot control. The present preliminary study shows the on-going results of the proposed methodology for both self-exploration of robotic morphologies and acquisition of human behaviours.

The work described here explores a neural network architecture that can be embedded directly in the realtime sensorimotor coordination loop of a developmental robot platform. We take inspiration from the way children are able to learn while interacting with a teacher, in particular the use of prediction of the teacher actions to improve own learning. The architecture is based on two neural networks that operate online, and in parallel, one for learning and one for prediction. A Hebbian learning rule is used to associate the high-dimensional afferent sensor input at different time-delays with the current efferent motor commands corresponding to the teacher demonstration. The predictions of future motor commands are used to limit the growth of the neural network weights, and to enable the robot to smoothly continue movements the teacher has begun. Results on a simulated iCub robot learning object interaction tasks are presented, including an analysis of the sensitivity to changes in the task setup. We also outline the first implementation on the real iCub platform.

This paper presents the release fv3 of the Complex Cellular Automata model Sciara for simulating lava flows. It is based on a Bingham-like rheology and both flow velocity and the physical time corresponding to a computational step have been made explicit. The model has been preliminary tested with satisfying results by considering the 2006 lava event at Mt Etna (Italy). A HTML5 based Web application with a WebGL 3D interactive visualization system has also been developed as user interface for Sciara-fv3. Eventually, the numerical code has been parallelised by the CUDA GPGPU paradigm, allowing for a considerable reduction of the execution time, despite the numerical code complexity.

This study presents an experiment that integrates a semiotic investigation with a dynamical perspective on embodied social interactions. The primary objective is to study co-creativity of a communication system on the human-human nonverbal interaction. Throughout the experiment, the results of established communication system on uni- and bi-directional interactions are compared. Finally, the role of co-creativity in the evolution of communication systems will be discussed.

Our aim of this paper is to investigate the human communication in terms of two following questions. One is how human can know the fact that an interacting partner is human. Another is how non-communicative behaviour can become communicative. To answer these questions, we performed two experiments exploiting the idea of perceptual crossing experiments. As a result, we will show that the turn-taking structure supports humanlikeness and human communication in the primitive non-verbal interaction. Our results will be discussed with ambient interface technology.

Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.

In this paper, a model based on Artificial Neural Networks (ANNs) extends the symbol grounding mechanism to abstract words for cognitive robots. The aim of this work is to obtain a semantic representation of abstract concepts through the grounding in sensorimotor experiences for a humanoid robotic platform. Simulation experiments have been developed on a software environment for the iCub robot. Words that express general actions with a sensorimotor component are first taught to the simulated robot. During the training stage the robot first learns to perform a set of basic action primitives through the mechanism of direct grounding. Subsequently, the grounding of action primitives, acquired via direct sensorimotor experience, is transferred to higher-order words via linguistic descriptions. The idea is that by combining words grounded in sensorimotor experience the simulated robot can acquire more abstract concepts. The experiments aim to teach the robot the meaning of abstract words by making it experience sensorimotor actions. The iCub humanoid robot will be used for testing experiments on a real robotic architecture.

Autonomous navigation and robust obstacle avoidance are prerequisites for the successful operation of a planetary rover. Typical approaches to tackling this problem rely on complex and computationally expensive navigation strategies based upon the creation of 3D maps of the environment. In contrast, this research proposes a simple artificial neural network relying on infrared sensory input as the control structure. This paper presents a unified framework for designing such control structures for a simulated rover, taking advantage of code parallelisation and the latest advances in global optimisation research. In particular, it details a 3D physics-based simulation of a planetary rover and a tool set for performing the optimisation of ANN parameters within the island model. This paper also presents preliminary results showing that the aforementioned framework can parallelise the controller design process without any loss in performance over traditional methods, and will outline research directions, which aim to take full advantage of this technique's potential.

The paper presents an evolutionary robotics model of the Rover Mars robot. This work has the objective to investigate the possibility of using an alternative sensor system, based on infrared sensors, for future rovers capable of performing autonomous tasks in challenging planetary terrain environments. The simulation model of the robot and of Mars terrain is based on a physics engine. The robot control system consists of an artificial neural network trained using evolutionary computation techniques. An adaptive threshold on the infrared sensors has been evolved together with the neural control system to allow the robot to adapt itself to many different environmental conditions. The properties of the behavior obtained after the evolutionary process has been tested by measuring the generalization performance of the rover under various terrain conditions and especially under rough terrain conditions. In addition, the dynamics of the co-evolution between the controller and the threshold has been analyzed. Those analyses show that different pathways have been explored by the evolutionary process in order to adapt the sensing abilities and the control system.

One of the advantages of sociality resides in the opportunity of exploiting the behavior of other individuals of the same group as a reliable source of information. In this paper we present an evolutionary simulation in which a population of 10 mobile robots has to develop a simple behavior consisting in the discrimination of two different foraging areas in the environment. We show that, given a minimal environmental pressure, a combination of learning oriented by social cues and selection at population level can lead to effective results. We further analyse the dynamic of the evolution of sociality, focusing on the fact that the global adaptiveness is a product of the combination of singularly nonadaptive processes and on the presence of a reinforcing positive feedback within populations, that is, the more dasiasocialpsila a population is, the more advantageous it is to exploit social cues in that population.

Social learning refers to the process in which agents learn, during their lifetime, new skills by interacting with other agents (for definitions and review of social learning in ethology see Zental...

In this paper, we present the results of an experiment in which a collection of simulated robots that have been evolved for the ability to solve a collective navigation problem develop a communication system that allows them to co-operate better. The analysis of the results obtained indicates how evolving robots develop a non-trivial communication system and exploit different communication modalities. The results also indicate how the possibility of co-adapting the robots’ individual and social/communicative behaviour plays a key role in the development of progressively more complex and effective individuals.