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. Mutual modelling, the reciprocal ability to establish a mental model of the other, plays a fundamental role in human interactions. This complex cognitive skill is however difficult to fully apprehend as it encompasses multiple neuronal, psychological and social mechanisms that are generally not easily turned into computational models suitable for robots. This article presents several perspectives on mutual modelling from a range of disciplines, and reflects on how these perspectives can be beneficial to the advancement of social cognition in robotics. We gather here both basic tools (concepts, formalisms, models) and exemplary experimental settings and methods that are of relevance to robotics. This contribution is expected to consolidate the corpus of knowledge readily available to human-robot interaction research, and to foster interest for this fundamentally cross-disciplinary field. While virtual humans are proven tools for training, education and research, they are far from realizing their full potential. Advances are needed in individual capabilities, such as character animation and speech synthesis, but perhaps more importantly, fundamental questions remain as to how best to integrate these capabilities into a single framework that allows us to efficiently create characters that can engage users in meaningful and realistic social interactions. This integration requires in-depth, inter-disciplinary understanding few individuals, or even teams of individuals, possess. We help address this challenge by introducing the ICT Virtual Human Toolkit, which offers a flexible framework for exploring a variety of different types of virtual human systems, from virtual listeners and question-answering characters to virtual role-players. We show that due to its modularity, the Toolkit allows researchers to mix and match provided capabilities with their own, lowering the barrier of entry to this multi-disciplinary research challenge. A revision of the #1 text in the Human Computer Interaction field, Interaction Design, the third edition is an ideal resource for learning the interdisciplinary skills needed for interaction design, human-computer interaction, information design, web design and ubiquitous computing.The authorsare acknowledged leaders and educators in their field, with a strong global reputation. They bring depth of scope to the subject in this new edition, encompassing the latest technologies and devices including social networking, Web 2.0 and mobile devices. The third edition also adds, develops and updates cases, examples and questions to bring the book in line with the latest in Human Computer Interaction.Interaction Design offers a cross-disciplinary, practical and process-oriented approach to Human Computer Interaction, showing not just what principles ought to apply to Interaction Design, but crucially how they can be applied. The book focuses on how to design interactive products that enhance and extend the way people communicate, interact and work. Motivating examples are included to illustrate both technical, but also social and ethical issues, making the book approachable and adaptable for both Computer Science and non-Computer Science users. Interviews with key HCI luminaries are included and provide an insight into current and future trends.The book has an accompanying website www.id-book.com which has been updated to include resources to match the new edition. Since 2000, Human Computer Interaction (HCI) has seen a turn to the artistic, looking at more provocative, cultural and social experiences. In doing so HCI is increasingly collaborating with artists who engage with real world data. Much of this work focuses on engaging the public in the spectacle of interactive experiences. In contrast, this paper takes a look behind the scenes by studying a collaboration between artists, climate scientists and researchers as they designed a participatory sensing system to interpret scientific data for public presentation. This paper presents this cross-disciplinary approach from the perspective of an artist researcher on the project. In this paper we present the results of a qualitative study with therapists to inform social robotics and human robot interaction (HRI) for engagement in rehabilitative therapies. Our results add to growing evidence that socially assistive robots (SARs) could play a role in addressing patients' low engagement with self-directed exercise programmes. Specifically, we propose how SARs might augment or offer more pro-active assistance over existing technologies such as smartphone applications, computer software and fitness trackers also designed to tackle this issue. In addition, we present a series of design implications for such SARs based on therapists' expert knowledge and best practices extracted from our results. This includes an initial set of SAR requirements and key considerations concerning personalised and adaptive interaction strategies. We introduce flash teams, a framework for dynamically assembling and managing paid experts from the crowd. Flash teams advance a vision of expert crowd work that accomplishes complex, interdependent goals such as engineering and design. These teams consist of sequences of linked modular tasks and handoffs that can be computationally managed. Interactive systems reason about and manipulate these teams' structures: for example, flash teams can be recombined to form larger organizations and authored automatically in response to a user's request. Flash teams can also hire more people elastically in reaction to task needs, and pipeline intermediate output to accelerate completion times. To enable flash teams, we present Foundry, an end-user authoring platform and runtime manager. Foundry allows users to author modular tasks, then manages teams through handoffs of intermediate work. We demonstrate that Foundry and flash teams enable crowdsourcing of a broad class of goals including design prototyping, course development, and film animation, in half the work time of traditional self-managed teams. We design and evaluate a method of human-robot cross-training, a validated and widely used strategy for the effective training of human teams. Cross-training is an interactive planning method in which team members iteratively switch roles with one another to learn a shared plan for the performance of a collaborative task. We first present a computational formulation of the robot mental model, which encodes the sequence of robot actions necessary for task completion and the expectations of the robot for preferred human actions, and show that the robot model is quantitatively comparable to the mental model that captures the inter-role knowledge held by the human. Additionally, we propose a quantitative measure of robot mental model convergence and an objective metric of model similarity. Based on this encoding, we formulate a human-robot cross-training method and evaluate its efficacy through experiments involving human subjects n = 60 . We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training yields statistically significant improvements in quantitative team performance measures, as well as significant differences in perceived robot performance and human trust. Finally, we discuss the objective measure of robot mental model convergence as a method to dynamically assess human errors. This study supports the hypothesis that the effective and fluent teaming of a human and a robot may best be achieved by modeling known, effective human teamwork practices. This paper presents data from a four month ethnographic study of professional pair programmers from two software development teams. Contrary to the current conception of pair programmers, the pairs in this study did not hew to the separate roles of "driver" and "navigator". Instead, the observed programmers moved together through different phases of the task, considering and discussing issues at the same strategic "range " or level of abstraction and in largely the same role. This form of interaction was reinforced by frequent switches in keyboard control during pairing and the use of dual keyboards. The distribution of expertise among the members of a pair had a strong influence on the tenor of pair programming interaction. Keyboard control had a consistent secondary effect on decisionmaking within the pair. These findings have implications for software development managers and practitioners as well as for the design of software development tools. Modern Internet services are often implemented as complex, large-scale distributed systems. These applications are constructed from collections of software modules that may be developed by different teams, perhaps in different programming languages, and could span many thousands of machines across multiple physical facilities. Tools that aid in understanding system behavior and reasoning about performance issues are invaluable in such an environment. Here we introduce the design of Dapper, Google’s production distributed systems tracing infrastructure, and describe how our design goals of low overhead, application-level transparency, and ubiquitous deployment on a very large scale system were met. Dapper shares conceptual similarities with other tracing systems, particularly Magpie [3] and X-Trace [12], but certain design choices were made that have been key to its success in our environment, such as the use of sampling and restricting the instrumentation to a rather small number of common libraries. The main goal of this paper is to report on our experience building, deploying and using the system for over two years, since Dapper’s foremost measure of success has been its usefulness to developer and operations teams. Dapper began as a self-contained tracing tool but evolved into a monitoring platform which has enabled the creation of many different tools, some of which were not anticipated by its designers. We describe a few of the analysis tools that have been built using Dapper, share statistics about its usage within Google, present some example use cases, and discuss lessons learned so far. Social psychology offers a perspective on the acceptance and adoption of technology that is not often considered in technical circles. In this paper, we discuss several adoption-of-technology models with respect to the acceptance of domestic robots: we examine social-psychology literature and apply it directly to human-robot interaction. We raise key points that we feel will be pivotal to how domestic users respond to robots, and provide a set of guidelines that roboticists and designers of robotic interfaces can use to consider and analyze their designs. Ultimately, understanding how users respond to robots and the reasons behind their responses will enable designers to creating domestic robots that are accepted into homes. Abstract The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in human-robot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and human-machine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed. Abstract We are concerned with the utility of social laws in a computational environment, laws which guarantee the successful coexistence of multiple programs and programmers. In this paper we are interested in the off-line design of social laws, where we as designers must decide ahead of time on useful social laws. In the first part of this paper we suggest the use of social laws in the domain of mobile robots, and prove analytic results about the usefulness of this approach in that setting. In the second part of this paper we present a general model of social law in a computational system, and investigate some of its properties. This includes a definition of the basic computational problem involved with the design of multi-agent systems, and an investigation of the automatic synthesis of useful social laws in the framework of a model which refers explicitly to social laws. This paper introduces swarm user interfaces, a new class of human-computer interfaces comprised of many autonomous robots that handle both display and interaction. We describe the design of Zooids, an open-source open-hardware platform for developing tabletop swarm interfaces. The platform consists of a collection of custom-designed wheeled micro robots each 2.6 cm in diameter, a radio base-station, a high-speed DLP structured light projector for optical tracking, and a software framework for application development and control. We illustrate the potential of tabletop swarm user interfaces through a set of application scenarios developed with Zooids, and discuss general design considerations unique to swarm user interfaces. This paper discusses the use of observational studies of human-robot social interaction in open human-inhabited environments as a method for improving on the design and evaluating the interactive capabilities of social robots. First, we discuss issues that have surfaced in attempts to evaluate social interactions between humans and robots. Next, we review two observational studies involving robots interacting socially with humans and discuss how the results can be applied to improving robot design. The first is an analysis of a mobile conference-attending robot that performed a search task by augmenting its perception through social interaction with human attendees. The second is an analysis of a stationary robotic receptionist that provides information to visitors and enhances interaction through story-telling. Through these examples, we show how observational studies can be applied to human-robot social interactions in varying contexts and with differing tasks to quantitatively and qualitatively evaluate (and discover unanticipated aspects of) the social interaction. Finally, we discuss design recommendations suggested by insights gained through these analyses We introduce a socially motivated interaction technique with collocated flying robots (a quadrotor in our current prototype). Instead of the traditional remote interaction controllers often used when interacting with flying robots and UAVs, we explore the collocated interaction space and suggest a direct interaction technique motivated by social human-robot interaction themes. Our approach is inspired by the types of interaction humans have with birds, specifically falconeering, and is facilitated by gestures-based interaction, while the user is within the field of view of the flying robot. This paper outlines our research goals, task examples, and our overall design approach. The paper also discusses our current prototyping efforts, as well as a preliminary evaluation of our approach, performed through two design critiques, studying our collocated interaction technique concept, and its potential, drawbacks and benefits for users. This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions.