In this paper we introduce the core results of the project on software development for social robotics systems. The usability of maintenance and control features is crucial for many kinds of systems, but in the case of social robotics we also have to take into account that (1) the humanoid robot physically interacts with humans, (2) the conversation with children might have different requirements in comparison to the conversation with adults. The results of our work were implement for the humanoid PAL REEM robot, but their core ideas can be applied for other types of humanoid robots. We developed a web-based solution that supports the management of robot-guided tours, provides recommendations for the users as well as allows for a visual analysis of the data on previous tours.
@cite_12 introduced a design framework enabling the development of social robotics applications by cross-disciplinary teams of programmers and interaction designers.
@cite_12
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We present a novel design framework enabling the development of social robotics applications by cross-disciplinary teams of programmers and interaction designers. By combining a modular back-end software architecture with an easy-to-use graphical interface for developing interaction sequences, this system enables programmers and designers to work in parallel to develop robot applications and tune the subtle details of social behaviors. In this paper, we describe the structure of our design framework, and we present an experimental evaluation of our system showing that it increases the effectiveness of programmerdesigner teams developing social robot applications. Keywords-human-robot interaction; software development; social robotics; programming interfaces; Automating domestic ironing is a challenge to the robotic community, particularly in terms of modelling and advanced mechanism design. This paper investigates the ironing process, its relevant folding algorithms and analysis techniques, presents the advanced mechanism synthesis and introduces cross‐disciplinary research. It summarises the second part of the results of a technology study carried out under an EPSRC grant “A Feasibility Study into Robotic Ironing”, and proposes new techniques in developing a folding and unfolding algorithm and in developing a task‐oriented mechanism synthesis for robotic ironing. Because of intense collaborative needs, requirements engineering is a challenge in global software development. How do distributed teams manage the development of requirements in environments that require significant cross-site collaboration and coordination? In this paper, we report research that used social network analysis to explore collaboration and awareness among team members during requirements management in an industrial distributed software team. Using the lens of a requirements-centred social network to group team members who work on a particular requirement, we collected data to characterize requirements-centric collaborations in a project, and to examine aspects of awareness of requirements changes within these networks. Our findings indicate organic patterns of collaboration involving considerable cross-site interaction, in which communication of changes was the most predominant reason for interaction. Although we did not find evidence that distance affects developers' awareness of remote team members who work on the same requirements, distance affected how accessible the remote colleagues were. We discuss implications for knowledge sharing and coordination of work on a requirement in distributed teams, and propose directions for the design of collaboration tools that support awareness in distributed requirements management. In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. @PARASPLIT We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner.