“No” is one of the first ten words used by children and embodies the first form of linguistic negation. Despite its early occurrence, the details of its acquisition remain largely unknown. The circumstance that “no” cannot be construed as a label for perceptible objects or events puts it outside the scope of most modern accounts of language acquisition. Moreover, most symbol grounding architectures will struggle to ground the word due to its non-referential character. The presented work extends symbol grounding to encompass affect and motivation. In a study involving the child-like robot iCub, we attempt to illuminate the acquisition process of negation words. The robot is deployed in speech-wise unconstrained interaction with participants acting as its language teachers. The results corroborate the hypothesis that affect or volition plays a pivotal role in the acquisition process. Negation words are prosodically salient within prohibitive utterances and negative intent interpretations such that they can be easily isolated from the teacher’s speech signal. These words subsequently may be grounded in negative affective states. However, observations of the nature of prohibition and the temporal relationships between its linguistic and extra-linguistic components raise questions over the suitability of Hebbian-type algorithms for certain types of language grounding.

The Care-O-bot is an autonomous robotic assistant that can support people in domestic and other environments. The behaviour of the robot can be defined by a set of high level control rules. The adoption and further development of such robotic assistants is inhibited by the absence of assurances about their safety. In previous work, formal models of the robot behaviour and its environment were constructed by hand and model checkers were then used to check whether desirable formal temporal properties were satisfied for all possible system behaviours. In this paper we describe the details of the software CRutoN, that provides an automatic translation from sets of robot control rules into input for the model checker NuSMV. We compare our work with previous attempts to formally verify the robot control rules, discuss the potential applications of the approach, and consider future directions of research.

In an era in which robots take a part in our lives in daily living activities, humans have to trust robots in home environments. We aim to create guidelines that allow humans to trust robots to be able to look after their well-being by adopting human-like behaviours. We want to study a Human-Robot Interaction (HRI) to assess whether a certain degree of transparency in the robots actions, the use of social behaviours and natural communications can affect humans' sense of trust and companionship towards the robots. However, trust can change over time due to different factors, e.g. due to perceiving erroneous robot behaviors. We believe that the magnitude and the timing of the error during an interaction may have different impacts resulting in different scales of loss of trust and of restoring lost trust.

Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naive human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.

Home Companion Robots need to be able to support users in their daily living activities and to be socially adaptive. They should take account of users' individual preferences, environments and social situations in order to behave in a socially acceptable manner and to gain acceptance into the household. They will need to be context-aware, taking account of any relevant contextual information and improve on delivering services by adapting to users' requirements. We present the design, implementation and technical evaluation of a Context-aware Proxemics Planner which aims to improve a robots' social behaviour by adapting its distances and orientation to the user in terms of interpersonal space, based on contextual information regarding the task, user and the robot.

It is essential for robots working in close proximity to people to be both safe and trustworthy. We present a case study on formal verification for a high-level planner/scheduler for the Care-O-bot, an autonomous personal robotic assistant. We describe how a model of the Care-O-bot and its environment was developed using Brahms, a multiagent workflow language. Formal verification was then carried out by automatically translating this model to the input language of an existing model checker. Four sample properties based on system requirements were verified. We then refined the environment model three times to increase its accuracy and the persuasiveness of the formal verification results. The first refinement uses a user activity log based on real-life experiments, but is deterministic. The second refinement uses the activities from the user activity log nondeterministically. The third refinement uses "conjoined activities" based on an observation that many user activities can overlap. The four samples properties were verified for each refinement of the environment model. Finally, we discuss the approach of environment model refinement with respect to this case study.

Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results.Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods.

This paper investigates users' preferred interaction modalities when playing an imitation game with KASPAR, a small child-sized humanoid robot. The study involved 16 adult participants teaching the robot to mime a nursery rhyme via one of three interaction modalities in a real-time Human-Robot Interaction (HRI) experiment: voice, guiding touch and visual demonstration. The findings suggest that the users appeared to have no preference in terms of human effort for completing the task. However, there was a significant difference in human enjoyment preferences of input modality and a marginal difference in the robot's perceived ability to imitate.

Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in “smart-home” environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and “nontechnical.” To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human-robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.

Previous research on social interaction among humans suggested that interpersonal motor coordination can help to establish social rapport. Our research addresses the question of whether, in a human-humanoid interaction experiment, the human's overall perception of a robot can be improved by realizing motor coordination behavior that allows the robot to adapt in real-time to a person's behavior. A synchrony detection method using information distance was adopted to realize the real-time human-robot motor coordination behavior, which guided the humanoid robot to coordinate its movements to a human by measuring the behavior synchrony between the robot and the human. The feedback of the participants indicated that most of the participants preferred to interact with the humanoid robot with the adaptive motor coordination capability. The results of this proof-of-concept study suggest that the motor coordination mechanism improved humans' overall perception of the humanoid robot. Together with our previous findings, namely that humans actively coordinate their behaviors to a humanoid robot's behaviors, this study further supports the hypothesis that bidirectional motor coordination could be a valid approach to facilitate adaptive human-humanoid interaction.

This paper discusses the concept of an Interaction History Architecture, its use in training a simple task on a mobile domestic robot and some negative results which point to limitations of such an approach. We begin with a brief history its use which motivated the current research. It is based upon Shannon Information Theory and has previously been used in both humanoid and non-humanoid robots. These studies hinted at the ability of using the Interaction History Architecture to classify actions on a broader scale. The experiment outlined is an early test-bed for the use of this on a domestic robot as well as introducing negative rewards to the system. We then present the results from this and discuss and explain some of the difficulties and limitations that were uncovered in this type of approach.

Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs, but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.

Demographics issues, characterised by an increasing elderly population, are expected to be a major concern both in Europe and other countries around the world. A proposed cost and care solution to these issues has been suggested that uses assistive robots in ’smarthome’ environments. The deployment of such integrated facilities presents many challenges, one of which concerns the customisation of such systems to meet the needs of the elderly person themselves. One approach is to allow the elderly person to actually teach the robot sufficient behaviours that meet their care requirements. The teaching could equally well be carried out by the elderly person’s relatives or carers. The overriding premise being that teaching is both intuitive and ’non-technical’. As part of a European project investigating these issues we have deployed a commercially available robot in a fully sensorised but otherwise ordinary suburban house, and designed a non-technical teaching system, based on behavioural templates, to achieve this goal. We have evaluated this integrated system within the house with 20 participants in a Human-Robot interaction experiment. Results indicate that participants overall found the interface easy to use, and felt that they would be capable of using it in a real-life situation. There were also some salient individual differences within the sample.

Robotic assistants are being designed to help, or work with, humans in a variety of situations from assistance within domestic situations, through medical care, to industrial settings. Whilst robots have been used in industry for some time they are often limited in terms of their range of movement or range of tasks. A new generation of robotic assistants have more freedom to move, and are able to autonomously make decisions and decide between alternatives. For people to adopt such robots they will have to be shown to be both safe and trustworthy. In this paper we focus on formal verification of a set of rules that have been developed to control the Care-O-bot, a robotic assistant located in a typical domestic environment. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether formal temporal properties are satisfied on all the possible behaviours of the system. We prove a number of properties relating to robot behaviours, their priority and interruptibility, helping to support both safety and trustworthiness of robot behaviours.

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.

This paper introduces the concept and use of an Interaction History Architecture for use on a mobile domestic robot and analyses the limitations of this configuration. The interaction history architecture builds upon Shannon information theory and has been previously used in a humanoid robot to learn basic children’s games. Previous work has shown that experience spaces can be highly flexible when used for learning. In this paper we outline and experiment designed to test the abilities of the architecture and how it can be used with classic clicker style training to teach domestic robots simple tasks. It then presents results from an experiment exploring these capabilities as well as the limitation found therein.

With changes in life expectancy across the world, technologies enhancing well-being of individuals, specifically for older people, are subject to a new stream of research and development. In this paper we present the ACCOMPANY project, a pan-European project which focuses on home companion technologies. The projects aims to progress beyond the state of the art in multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, monitoring persons and chores at home, and technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 and in the context of a smart-home environment utilising a multitude of sensor arrays. The resulting prototype from integrating these developments undergoes multiple formative cycles and a summative evaluation cycle towards identifying acceptable behaviours and roles for the robot for example role as a butler or a trainer. Furthermore, the evaluation activities will use an evaluation grid in order to assess achievement of the identified user requirements, formulated in form of distinct scenarios. Finally, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developments.

Robotic assistants are being developed to assist with a range of tasks at work and home. Besides designing and developing such robotic assistants, a key issue that needs to be addressed is showing that they are both safe and trustworthy. We discuss our approach to this using formal verification, simulation-based testing and formative user evaluation.