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In this paper, a cable-driven hyper-redundant manipulator using leader-follower control is explored. The proposed system is being developed for the purpose of exploration and inspection of highly confined spaces. Using a tele-operated joystick, an operator will have direct control over the end-effector, which will determine the trajectory and motion of the robotic system through the leader-follower control. To develop a control system for the manipulator, the kinematic relationships between the cables, motors and joints were firstly explored. Based on these relationships the system is modelled mathematically from the user to the robot end-effector. Using the kinematic equations, a control system was developed in MATLAB simulink. A prototype was developed to measure and validate kinematic relationships between the cables and joints of the system. An Error detection and correction mechanism is implemented using proportional control, validating the Kp value using the prototype system. To verify the kinematics and the proposed control system, a simulation was conducted using the MATLAB robotic toolbox. The simulation result demonstrated the promising capability of the proposed leader-follower control system in controlling the robot motion and its trajectory.

This paper presents a contribution to the active field of robotics research to support the development of social skills and capabilities in children with Autism Spectrum Disorders as well as Typically Developing children. We present preliminary results of a novel experiment where classical roles are reversed: children are here the teachers giving positive or negative reinforcement to the Kaspar robot to make it learn arbitrary associations between toys and locations where to tidy them. The goal is to help children change perspective, and understand that sometimes a learning agent needs several repetitions before correctly learning something. We developed a reinforcement learning algorithm enabling Kaspar to verbally convey its uncertainty along learning, so as to better inform the interacting child of the reasons behind successes and failures made by the robot. Overall, 30 children aged between 7 and 8 (19 girls, 11 boys) performed 16 sessions of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. Kaspar only made a few unexpected associations, mostly due to exploratory choices, and eventually reached minimal uncertainty. All children expressed enthusiasm in the experiment.

Robot-assisted therapy has been successfully used to help children with Autism Spectrum Condition (ASC) develop their social skills, but very often with the robot being fully controlled remotely by an adult operator. Although this method is reliable and allows the operator to conduct a therapy session in a customised child-centred manner, it increases the cognitive workload on the human operator since it requires them to divide their attention between the robot and the child to ensure that the robot is responding appropriately to the child's behaviour. In addition, a remote-controlled robot is not aware of the information regarding the interaction with children (e.g., body gesture and head pose, proximity etc) and consequently it does not have the ability to shape live HRIs. Further to this, a remote-controlled robot typically does not have the capacity to record this information and additional effort is required to analyse the interaction data. For these reasons, using a remote-controlled robot in robot-assisted therapy may be unsustainable for long-term interactions. To lighten the cognitive burden on the human operator and to provide a consistent therapeutic experience, it is essential to create some degrees of autonomy and enable the robot to perform some autonomous behaviours during interactions with children. Our previous research with the Kaspar robot either implemented a fully autonomous scenario involving pairs of children, which then lacked the often important input of the supervising adult, or, in most of our research, has used a remote control in the hand of the adult or the children to operate the robot. Alternatively, this paper provides an overview of the design and implementation of a robotic system called Sense- Think-Act which converts the remote-controlled scenarios of our humanoid robot into a semi-autonomous social agent with the capacity to play games autonomously (under human supervision) with children in the real-world school settings.

How experienced emotional states, induced by the events that emerge in our context, influence our behaviour? Are they an obstacle or a helpful assistant for our reasoning process? Antonio Damasio gave exhaustive answers to these questions through his studies on patients with brain injuries. He demonstrated how the emotions guide decision-making and he has identified a region of the brain which has a fundamental role in this process. Antoine Bechara devised a test to validate the proper functioning of that cortical region of the brain. Inspired from Damasio’s theories we developed a mechanism in an artificial agent that enables it to represent emotional states and to exploit them for biasing its decisions. We also implement the card gambling task that Bechara used on his patients as a validating test. Finally we put our artificial agent through this test for 100 trials. The results of this experiment are analysed and discussed highlighting the demonstrated efficiency of the implemented somatic marker mechanism and the potential impact of this system in the field of social robotics.

Economic Load Dispatch (ELD) is one of an important optimization tasks which provides an economic condition for a power systems. In this paper, Particle Swarm Optimization (PSO) as an effective and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. In this paper, a piecewise quadratic function is used to show the fuel cost equation of each generation units, and the B-coefficient matrix is used to represent transmission losses. The feasibility of the proposed method to show the performance of this method to solve and manage a constraint problems is demonstrated in 4 power system test cases, consisting 3,6,15, and 40 generation units with neglected losses in two of the last cases. The obtained PSO results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) base approaches. These results prove that the proposed method is capable of getting higher quality solution including mathematical simplicity, fast convergence, and robustness to solve hard optimization problems.

Computer, tablet and smartphone are tools that increasingly accompany us during everyday activities. Given the booming use of the virtual reality and the wide range of people who have access to it, people are increasingly presented with an online alternative to the support of professionals, therapeutic groups organized by healthcare institutions, or significant others (such as family, friends and colleagues). This can be used as a tool for personal development and to cope with stress. Our research program includes creating a virtual reality application to sustain well-being and improve quality of life. It assumes that avatars, representations of a person in the cyberspace, will provide support in the form of a virtual conversation. Dialogue with an imaginary person is as a supportive technique in a stressful situation as creating the list of solutions and on a long term period it can create a specific way to reach the desired change.

Building a social robot that is able to interact naturally with people is a challenging task that becomes even more ambitious if the robots’ interlocutors are children involved in crowded scenarios like a classroom or a museum. In such scenarios, the main concern is enabling the robot to track the subjects’ social and affective state modulating its behaviour on the basis of the engagement and the emotional state of its interlocutors. To reach this goal, the robot needs to gather visual and auditory data, but also to acquire physiological signals, which are fundamental for understating the interlocutors’ psycho-physiological state. Following this purpose, several Human-Robot Interaction (HRI) frameworks have been proposed in the last years, although most of them have been based on the use of wearable sensors. However, wearable equipments are not the best technology for acquisition in crowded multi-party environments for obvious reasons (e.g., all the subjects should be prepared before the experiment by wearing the acquisition devices). Furthermore, wearable sensors, also if designed to be minimally intrusive, add an extra factor to the HRI scenarios, introducing a bias in the measurements due to psychological stress. In order to overcome this limitations, in this work, we present an unobtrusive method to acquire both visual and physiological signals from multiple subjects involved in HRI. The system is able to integrate acquired data and associate them with unique subjects’ IDs. The implemented system has been tested with the FACE humanoid in order to assess integrated devices and algorithms technical features. Preliminary tests demonstrated that the developed system can be used for extending the FACE perception capabilities giving it a sort of sixth sense that will improve the robot empathic and behavioural capabilities.

Visual Perspective Taking (VPT) is the ability to see the world from another person’s perspective, taking into account what they see and how they see it, drawing upon both spatial and social information. Children with autism often find it difficult to understand that other people might have perspectives, viewpoints, beliefs and knowledge that are different from their own which is a fundamental aspect VPT. In this paper, we present the piloting of scenarios for our first large scale pilot-study using a humanoid robot to assist children with autism develop their VPT skills. The games were implemented with the Kaspar robot and to our knowledge this is the first attempt to improve the VPT skills of children with autism through playing and interacting with a humanoid robot.

This paper gives an overview of the design and development of the humanoid robot Kaspar. Since the first Kaspar robot was developed in 2005, the robotic platform has undergone continuous development driven by the needs of users and technological advancements enabling the integration of new features. We discuss in detail the iterative development of Kaspar’s design and clearly explain the rational of each development, which has been based on the user requirements as well as our years of experience in robot assisted therapy for children with autism, particularly focusing on how the developments benefit the children we work with. Further to this, we discuss the role and benefits of robotic autonomy on both children and therapist along with the progress that we have made on the Kaspar robot’s autonomy towards achieving a semi-autonomous child-robot interaction in a real world setting.

A context-aware attention system is fundamental for regulating the robot behaviour in a social interaction since it enables social robots to actively select the right environmental stimuli at the right time during a multiparty social interaction. This contribution presents a modular context-aware attention system which drives the robot gaze. It is composed by two modules: the scene analyzer module manages incoming data flow and provides a human-like understanding of the information coming from the surrounding environment; the attention module allows the robot to select the most important target in the perceived scene on the base of a computational model. After describing the motivation, we report the proposed system and the preliminary test.

Two perspectives define a human being in his social sphere: appearance and behaviour. The aesthetic aspect is the first significant element that impacts a communication while the behavioural aspect is a crucial factor in evaluating the ongoing interaction. In particular, we have more expectations when interacting with anthropomorphic robots and we tend to define them believable if they respect human social conventions. Therefore researchers are focused both on increasingly anthropomorphizing the embodiment of the robots and on giving the robots a realistic behaviour.

Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.

The study described in this article is part of our contribution to the Horizon 2020 Babyrobot project, where we have created different play scenarios for children with autism to playfully explore elements that are important in developing Visual Perspective Taking (VPT) skills. Individuals with autism often have difficulty with Theory of Mind (TOM) and the understanding that other individuals have their own thoughts, beliefs, plans and perspectives. Visual Perspective Taking is the ability to view the world from another individual’s perspective, e.g. understanding that other individuals have a different line of sight to oneself, and also understanding that two or more people viewing the same object from different points in space might see different things. It is believed that TOM and VPT may share common cognitive processes. Our study aims to help children with autism develop their VPT skills using the Kaspar robot. Using a robot to teach children about VPT has a distinct advantage in the fact that what the robot can see can be shown directly to the children using the cameras in the robot’s eyes and a screen to show the robot’s perspective. This article presents the preliminary test of these concepts during interaction sessions with children. It also presents the testing and further development of the play scenarios specifically in aspects related to VPT.

The creation of social empathie communication channels between social robots and humans has started to become reality. Nowadays, the development of empathie and affective agents is giving to scientists another way to explore the social dimension of human beings. In this work, we introduce the FACE humanoid project that aims at creating a social and emotional android. FACE is an android head with an articulated neck mounted on a passive body. In order to enable FACE to perceive and express emotions, two dedicated engines have been developed. A sensory apparatus able to perceive the "social world", and a facial expressions generation engine that allows the robot to express its synthetic emotions. The system has been also integrated with an attention-based gaze generation component that allows the robot to autonomously follow a conversation between its partners. The developed framework has been implemented and tested in several standard human-robot interaction settings. Results demonstrated the promising social capabilities of the robot to perceive and convey emotions to humans through the generation of emotional perceivable facial expressions and socially aligned behaviour.

Human gaze and blinking behaviours have been recently considered, to empower humanlike robots to convey a realistic behaviour in a social human-robot interaction. This paper reports the findings of our investigation on human eye-blinking behaviour in relation to human gaze behaviour, in a human-human interaction. These findings then can be used to design a humanlike eye-blinking model for a social humanlike robot. In an experimental eye-tracking study, we showed to 11 participants, a 7-minute video of social interactions of two people, and collected their eye-blinking and gaze behaviours with an eye-tracker. Analysing the collected data, we measured information such as participants’ blinking rate, maximum and minimum blinking duration, number of frequent (multiple) blinking, as well as the participants’ gaze directions on environment. The results revealed that participants’ blinking rate in a social interaction are qualitatively correlated to the gaze behaviour, as higher number of gaze shift increased the blinking rate. Based on the findings of this study, we can propose a context-dependent blinking model as an important component of the robot’s gaze control system that can empower our robot to mimic human blinking behaviour in a multiparty social interaction.

The study described in this paper investigated the effects of previous exposure to robots on children’s perception of the Kaspar robot. 166 children aged between 7 and 11 participated in the study in the framework of a UK robotics week 2018 event, in which we visited a local primary school with a number of different robotic platforms to teach the children about robotics. Children’s perception of the Kaspar robot was measured using a questionnaire following a direct interaction with the robot in a teaching scenario. Children’s previous exposure to other robots and Kaspar itself was manipulated by controlling the order of children’s participation in the different activities over the event. Effects of age and gender were also examined. Results suggest significant effects of previous exposure and gender on children’s perception of Kaspar, while age had no significant effect. Important methodological implications for future studies are discussed.

Autism Spectrum Disorder (ASD) is a neural development disorder characterized by specific patterns of behavioral and social difficulties. Beyond these core symptoms, additional problems such as absence of gender differences identification, interactional distortions of environmental and family responses are often present. Taking into account these emotional and behavioral problems researchers and clinicians are focusing on the design of innovative therapeutic approaches aimed to improve social capabilities of subjects with ASD. Thanks to the technological and scientific progresses of the last years, nowadays it is possible to create human-like robots with social and emotional capabilities. Furthermore it is also possible to analyze physiological signals inferring subjects' psycho-physiological state which can be compared with a behavioral analysis in order to obtain a deeper understanding of subjects reactions to treatments. In this work a preliminary evaluation of an innovative social robot-based treatment for subjects with ASD is described. The treatment consists in a complex stimulation and acquisition platform composed of a social robot, a multi-parametric acquisition system and a therapeutic protocol. During the preliminary tests of the treatment the subject's physiological signals and behavioral parameters have been recorded and used together with the therapists' annotations to infer the subjects' induced reactions. Physiological signals were analyzed and statistically evaluated demonstrating the possibility to correctly discern the two groups (ASD and normally developing subjects) with a classification percentage higher than 92%. Statistical analysis also highlighted the treatment capability to induce different affective states in subjects with ASDs more than in control subjects, demonstrating that the treatment is well designed and tuned on ASDs deficits and behavioral lacks.

Sensing and interpreting the interlocutor’s social behaviours is a core challenge in the development of social robots. Social robots require both an innovative sensory apparatus able to perceive the “social and emotional world” in which they act and a cognitive system able to manage this incoming sensory information and plan an organized and pondered response. In order to allow scientists to design cognitive models for this new generation of social machines, it is necessary to develop control architectures that can be easily used also by researchers without technical skills of programming such as psychologists and neuroscientists. In this work an innovative hybrid deliberative/reactive cognitive architecture for controlling a social humanoid robot is presented. Design and implementation of the overall architecture take inspiration from the human nervous system. In particular, the cognitive system is based on the Damasio’s thesis. The architecture has been preliminary tested with the FACE robot. A social behaviour has been modeled to make FACE able to properly follow a human subject during a basic social interaction task and perform facial expressions as a reaction to the social context.

Children with autism often find it difficult to understand that other people might have perspectives, viewpoints, beliefs and knowledge that are different from their own. One fundamental aspect of this difficulty is Visual Perspective Taking (VPT). Visual perspective taking is the ability to see the world from another person's perspective, taking into account what they see and how they see it, drawing upon both spatial and social information. In this paper, we outline the child-robot interaction scenarios that we have developed as part of the European BabyRobot project to assist children with autism explore elements that are important in developing VPT skills. Further to this we describe the standard pre and post assessments that we will perform with the children in order to measure their progress. The games were implemented with the Kaspar robot. To our knowledge this is the first attempt to improve the VPT skills of children with autism through playing and interacting with a humanoid robot.

This paper presents a contribution to the active field of robotics research to support the development of social skills and capabilities in children with Autism Spectrum Disorders as well as Typically Developing children. We present preliminary results of a novel experiment where classical roles are reversed: children are here the teachers giving positive or negative reinforcement to the Kaspar robot to make it learn arbitrary associations between toys and locations where to tidy them. The goal is to help children change perspective, and understand that sometimes a learning agent needs several repetitions before correctly learning something. We developed a reinforcement learning algorithm enabling Kaspar to verbally convey its uncertainty along learning, so as to better inform the interacting child of the reasons behind successes and failures made by the robot. Overall, 30 children aged between 7 and 8 (19 girls, 11 boys) performed 16 sessions of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. Kaspar only made a few unexpected associations, mostly due to exploratory choices, and eventually reached minimal uncertainty. All children expressed enthusiasm in the experiment.