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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. The developed system has been implemented on the humanoid robot Kaspar and evaluated in a trial with four children with ASC at a local specialist secondary school in the UK where the data of 11 Child-Robot Interactions (CRIs) was collected. The results from this trial demonstrated that the system was successful in providing the robot with appropriate control signals to operate in a semi-autonomous manner without any latency, which supports autonomous CRIs, suggesting that the proposed architecture appears to have promising potential in supporting CRIs for real-world applications.

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.

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.

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.

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