Reliability, safety, and robustness are essential requirements for safety critical applications.Implementing these properties in artificial intelligence based systems introduces additional challenges, particularly in the development and validation of data driven models.To address these requirements, new techniques are needed for assessing predictive uncertainty, ensuring robustness against intentional or environmental input perturbations, integrating explicit safety constraints into model architectures, and enabling human oversight and interpretability to support auditing and supervision.

Social robots interacting with people in public spaces may access and collect their personal information, which raises privacy concerns regarding the disclosure of personal information. This paper aims to investigate factors impacting individuals’ intention to disclose personal information to a social robot in public spaces and evaluate the actual disclosure during the interaction with the robot. For this purpose, a model is proposed to predict people’s intentions to disclose information to a social robot. We conducted our experiment at a public festival with more than 100 participants using the social robot ARI. The findings reveal the substantial impact of factors including risk beliefs, trusting beliefs, perceived enjoyment, and social influence on the intention to disclose personal information. Moreover, they reveal that although only a small percentage (6.20%) of people had the intention to disclose information to the social robot, most participants (98.00%) finally disclosed their personal information.

This article introduces “Baby Robot”, a robot designed to improve infants' and toddlers' motor skills. This robot is a car-like toy that moves autonomously by using reinforcement learning and computer vision. Its behaviour consists of escaping from a target infant that has been previously recognized, or at least detected, without compromising the infant's security by avoiding obstacles. Regarding other robots that share this purpose, there is a variety of commercial toys available on the market; however, no one is betting on an intelligent autonomous movement, since they use to repeat simple, yet repetitive movements. In order to examine how that autonomous movement may improve infants' mobility, two crawling toys-one in representation of “Baby Robot” - were tested in a real environment. These real-life experiments were conducted with a safe and approved surrogate of our proposed robot in a kindergarten, where a group of infants interacted with the toys. Improvements in the efficiency of the play-sassion were detected.

In this WIP-Innovation Practice, the authors present a shared Collaborative Online Robotics platform embedded in Google Slides application for students from primary grades to University to enhance the learning and teaching process in online and blended/hybrid environments. This Computer Supported Cooperative Work (CSCW) system is intended to facilitate an immersive experience in a collaborative virtual environment by combining physical digital artifacts with remote STEM-based instruction. Particularly relevant during COVID-19, it also helps connect students potentially isolated by other factors (geographic, economic, environmental, health limitations, etc.). In this paper we present a first approach to the performance, acceptance, and adherence to a novel remote collaborative platform that facilitates STEM and Social Emotional Learning (SEL) enhanced by the interaction of a multiple-users with a LEGO MINDSTORM EV3 Robotic platform based on the Positive Technological Development (PTD) framework.

The problem of finding a suitable qualitative representation for robots to reason about activity spaces where they carry out tasks such as leading or interacting with a group of people is tackled in this paper. For that, a Qualitative Spatial model for Group Robot Interaction (QS-GRI) is proposed to define Kendon’s F-formations [Kendon, 2010] depending on: (i) the relative location of the robot with respect to other individuals involved in that interaction; (ii) the individuals’ orientation; (iii) the shared peri-personal distance; and (iv) the role of the individuals (observer, main character or interactive). An iconic representation is provided and Kendon’s formations are defined logically. The conceptual neighborhood of the evolution of Kendon formations is studied, that is, how one formation is transformed into another. These transformations can depend on the role that the robot have, and on the amount of people involved.

Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy linguistic terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.

The continued growth in enterprise social networks is fueled by the need to enable productivity and innovation. Reducing the constraints to communication and knowledge sharing of a globally distributed workforce, will facilitate the workflow. People finder systems are one of the main solutions in enterprise social networks which are reducing these constraints leading to time and cost savings. Finding expertise efficiently helps organizations to unlock knowledge within the enterprise, solve problems, and identify collaborators. However, the following challenges still exist: validating expertise, determining responsiveness and accessibility, and managing expert profiles. In this paper, we propose the fuzzy OWA technique as a novel approach to ranking candidates in expertise search. We consider its application in open innovation intermediaries where the search for partners and expertise is at the center of the business model. In addition, we demonstrate its application in a software tool (InsERT) as part of a larger enterprise social network implementation.

The emergence of robot applications for non-technical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft's Kinect™v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case. A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates.

A humanoid service robot equipped with a set of simple action skills including navigating, grasping, recognising objects or people, among others, is considered in this paper. By using those skills the robot should complete a voice command expressed in natural language encoding a complex task (defined as the concatenation of a number of those basic skills). As a main feature, no traditional planner has been used to decide skills to be activated, as well as in which sequence. Instead, the SOAR cognitive architecture acts as the reasoner by selecting which action the robot should complete, addressing it towards the goal. Our proposal allows to include new goals for the robot just by adding new skills (without the need to encode new plans). The proposed architecture has been tested on a human-sized humanoid robot, REEM, acting as a general purpose service robot.

In order to observe spatial relationships in social human-robot interactions, a field trial was carried out within the CosmoCaixa Science Museum in Barcelona. The follow me episodes studied showed that the space configurations formed by guide and visitors walking together did not always fit the robot social affordances and navigation requirements to perform the guidance successfully, thus additional communication prompts are considered to regulate effectively the walking together and follow me behaviours.

This paper presents an analysis of two comparable studies with LEGO Robotics-based activities in a social skills training program for children with autism spectrum disorders (ASD). One study has been carried out with a group of 16 children in the Unit of Pediatrics Psychology and Psychiatry in HSJD in Barcelona, Spain and the other with a group of 17 children at the Center for Education and Engineering Outreach (Tufts U.) in Boston, USA. The aim of this comparison is discuss lessons learnt and develop empirical based guidelines for intervention design.

Emotional factors related to aging at home assistive technology are known to affect technology acceptance, effective use, and quality of life improvement. This paper is a survey on the affective dimension of robot-based systems conceived for helping elderly at home. The specificity of elders' capabilities (e.g. sensory and cognitive), coping styles, aspirations, lifestyles, social rules and preferences are faced with available knowledge from the fields of social psychology, sociology and gerontology. In the case of social robots, convenient verbal and non-verbal communication and motion behavior (e.g. social distance, space formations) are to be designed according to generational and cultural rules. Moreover, robot behavior should be congruent with its role (i.e. helper, companion) and affordances.

This paper presents a study on (1) how children experience a pet robot, (2) how they play with it and (3) how children's perceptions on and interaction with pet robots are interrelated. The study features different types of subjective and objective techniques to assess the degree of perceived social entity from self-reports (i.e. questionnaires) and observed behavior. Three short questionnaires and an ad hoc code scheme of 15 low-level micro-behaviors were developed. 28 scholars aged 8 to 12 were observed at school during a play period with a Pleo robot and asked to answer the questionnaires. We found that the different questionnaire based methods were in line each other. Therefore, anyone of them can be used to measure the experience of a social entity. Play analyses showed that the two most prevalent behaviors were clearly social: petting the robot and showing it objects to engage in interaction. Moreover, children spent on average less than one per cent of the session time treating the robot as an artifact. However, significant covariation between the experience of a social entity and observed behavior could not be established.

This work explores the dynamics of the emergence of the social bonds with robots. A field study with 49 sixth grade scholars (aged 11-12 years) and 4 different robots was carried out at an elementary school. A subsequent laboratory experiment with 4 of the participants was completed. For the first experience, at school children's preferences, expectancies on functionality and communication, and interaction behavior were studied. Using the data collected in the laboratory, recognition, the selection of partner, and dyadic interaction were explored. Both at school and in the lab, data from videotaped direct observation, questionnaires and interviews were gathered. The results showed that different appearance and performance of robots elicit in children distinctive perceptions and interactive behavior, and affect social processes, such as role attribution and attachment. This work presents a preliminary field study to explore the introduction of robot-based programs to improve the quality of life of hospitalized children.

Algorithms for automatic fall detection are often studied in the field of ambulatory human health supervision. These algorithms are developed to generate hospital emergency alarms. In the present paper, involuntary lateral movements (ILM) are presented. ILM are a premature sign of health deterioration. Therefore, this algorithm embedded in a sensor device could be used for continuous health monitoring in ambulatory situations. Several studies show that human bodies try to minimize acceleration body movements, so they are based on minimum jerk. The proposed algorithm is based on a jerk threshold detection. In this work it is supposed that ILM will produce important jerk values above other daily movements, so that they can be distinguished using a threshold.

This article introduces a new method for supervised discretization based on interval distances by using a novel concept of neighbourhood in the target's space. The method proposed takes into consideration the order of the class attribute, when this exists, so that it can be used with ordinal discrete classes as well as continuous classes, in the case of regression problems. The method has proved to be very efficient in terms of accuracy and faster than the most commonly supervised discretization methods used in the literature. It is illustrated through several examples and a comparison with other standard discretization methods is performed for three public data sets by using two different learning tasks: a decision tree algorithm and SVM for regression.

During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance.

Focusing on classification learning, this book covers learning algorithms and learning theory. The book concludes with appendices covering some of the technical aspects involved. The book is a good reference for scientists and engineers interested in learning about kernel classifiers. It is not very suitable as a primary student text, but is recommended as secondary reading for students requiring an in-depth insight into this area.

A coordination control structure is employed to manipulate the dynamics of a simulated 3D articulated mobile robot. Coordination emerges as the interaction between robot joints, its parameters are computed using a policy gradient reinforcement learning process, obtaining a machine extrapolation of human cognitive behavior: The interplay of brain, body and environment. A simulated Hoap-2 humanoid is trained to achieve an equilibrium task through experience. The system learns how to change the dynamics of the articulations to assess coordination, avoiding to fall down during the execution of the task. The robot is able to automatically compute their motions from high-level descriptions of tasks, without the use of pre-established models, but with ones acquired by sensing.

July 01 2007 Webots Simulator 5.1.7. Cyberbotics Ltd. (2006). Available in different versions at different prices; CHF 3600 ($2940) for the PRO version, normal price In Special Collection: CogNet R. Téllez, R. Téllez * Corresponding author. Search for other works by this author on: This Site Google Scholar C. Angulo C. Angulo Search for other works by this author on: This Site Google Scholar Author and Article Information R. Téllez C. Angulo * Corresponding author. Online ISSN: 1530-9185 Print ISSN: 1064-5462 © 2007 Massachusetts Institute of Technology2007 Artificial Life (2007) 13 (3): 313–318. https://doi.org/10.1162/artl.2007.13.3.313 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation R. Téllez, C. Angulo; Webots Simulator 5.1.7. Cyberbotics Ltd. (2006). Available in different versions at different prices; CHF 3600 ($2940) for the PRO version, normal price. Artif Life 2007; 13 (3): 313–318. doi: https://doi.org/10.1162/artl.2007.13.3.313 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2007 Massachusetts Institute of Technology2007 Article PDF first page preview Close Modal You do not currently have access to this content.

In this article, we propose some methods for deriving symbolic interpretation of data in the form of rule based learning systems by using Support Vector Machines (SVM). First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for performing rule extraction. By using support vectors from a learned SVM it is possible in our approach to use any standard Radial Basis Function (RBF) learning technique for the rule extraction, whilst avoiding the overlapping between classes problem. We will show that merging node centers and support vectors explanation rules can be obtained in the form of ellipsoids and hyper-rectangles. Next, in a dual form, following the framework developed for RBFNN, we construct an algorithm for SVM. Taking SVM as the main paradigm, geometry in the input space is defined from a combination of support vectors and prototype vectors obtained from any clustering algorithm. Finally, randomness associated with clustering algorithms or RBF learning is avoided by using only a learned SVM to define the geometry of the studied region. The results obtained from a certain number of experiments on benchmarks in different domains are also given, leading to a conclusion on the viability of our proposal.