“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.

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.

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.

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.

We overview how sensorimotor experience can be operationalized for interaction scenarios in which humanoid robots acquire skills and linguistic behaviours via enacting a “form-of-life” in interaction games (following Wittgenstein) with humans. The enactive paradigm is introduced which provides a powerful framework for the construction of complex adaptive systems, based on interaction, habit, and experience. Enactive cognitive architectures (following insights of Varela, Thompson and Rosch) that we have developed support social learning and robot ontogeny by harnessing information-theoretic methods and raw uninterpreted sensorimotor experience to scaffold the acquisition of behaviours. The success criterion here is validation by the robot engaging in ongoing human-robot interaction with naive participants who, over the course of iterated interactions, shape the robot's behavioural and linguistic development. Engagement in such interaction exhibiting aspects of purposeful, habitual recurring structure evidences the developed capability of the humanoid to enact language and interaction games as a successful participant.

The paper investigates the effects of a robot's “on-line” feedback during a tutoring situation with a human tutor. Analysis is based on a study conducted with an iCub robot that autonomously generates its feedback (gaze, pointing gesture) based on the system's perception of the tutor's actions using the idea of reciprocity of actions. Sequential micro-analysis of two opposite cases reveals how the robot's behavior (responsive vs. non-responsive) pro-actively shapes the tutor's conduct and thus co-produces the way in which it is being tutored. A dialogic and a monologic tutoring style are distinguished. The first 20 seconds of an encounter are found to shape the user's perception and expectations of the system's competences and lead to a relatively stable tutoring style even if the robot's reactivity and appropriateness of feedback changes.

This paper describes a system that simulates word form acquisition without meaning as a preliminary stage in language acquisition, through interaction with a human teacher. The system extracts phonemes from the teacher's speech and supplies them to a linguistically enabled synthetic agent. The agent babbles initially but gradually, words begin to emerge as the agent biases its babble towards the speech of the teacher. Experiments are conducted in real-time with a human teacher interacting with the agent embodied in the iCub humanoid robot.

It is thought that meaning may be grounded in early childhood language learning via the physical and social interaction of the infant with those around him or her, and that the capacity to use words, phrases and their meaning are acquired through shared referential dasiainferencepsila in pragmatic interactions. In order to create appropriate conditions for language learning by a humanoid robot, it would therefore be necessary to expose the robot to similar physical and social contexts. However in the early stages of language learning it is estimated that a 2-year-old child can be exposed to as many as 7,000 utterances per day in varied contextual situations. In this paper we report on the issues behind and the design of our currently ongoing and forthcoming experiments aimed to allow a robot to carry out language learning in a manner analogous to that in early child development and which effectively dasiashort cutspsila holophrase learning. Two approaches are used: (1) simulated babbling through mechanisms which will yield basic word or holophrase structures and (2) a scenario for interaction between a human and the humanoid robot where shared dasiaintentionalpsila referencing and the associations between physical, visual and speech modalities can be experienced by the robot. The output of these experiments, combined to yield word or holophrase structures grounded in the robot's own actions and modalities, would provide scaffolding for further proto-grammatical usage-based learning. This requires interaction with the physical and social environment involving human feedback to bootstrap developing linguistic competencies. These structures would then form the basis for further studies on language acquisition, including the emergence of negation and more complex grammar.

In this paper we describe three lines of research related to the issue of helping robots imitate people. These studies are based on observed human behaviour, technical metrics and implemented technical solutions. The three lines of research are: (a) a number of user studies that show how humans naturally tend to demonstrate a task for a robot to learn, (b) a formal approach to tackle the problem of what a robot should imitate, and (c) a technology-driven conceptual framework and technique, inspired by social learning theories, that addresses how a robot can be taught. In this merging exercise we will try to propose a way through this problem space, towards the design of a Human-Robot Interaction (HRI) system able to be taught by humans via demonstration.

Investigating how people respond to and relate to robots is a multifaceted scientific challenge. This paper reports on an experimental investigation concerning movement interference effects between a human and a robot. We compare results with that obtained by Oztop et al. [1], however, in our study we used a small child-sized robot (KASPAR) with an overall human-like appearance. The experiment was conducted with both child and adult participants who interacted with a small humanoid robot using arm waving behaviours. The experimental setup was designed to be less constrained than in [1] with an emphasis on playful interaction. The experimental results did not show evidence for interference effects. This might be due to a more game-like and less constrained experimental environment or to the specific features of the robot or both. In addition to measurements of the variance of the movements, we investigated a measure for behavioural synchrony between human and robot movements based on the concept of information distance. The results of information distance analysis indicated that most of the human participants were affected by the robot's behavioural rhythms. While our experiments did not show a movement interference effect, we found behavioural adaptation of participants' movement timing to the robot's movements. Thus, the measure of behavioural synchrony that we introduced appears useful for complementing other measures (such as variance) previously used in the literature.

Imitation is a powerful mechanism that allows agents to learn via their interactions within a social context. An artificial system that is capable of exploiting this imitative learning capability would be able to acquire new skills and tasks from interaction with another agent (typically a human or another robot). Imitative social learning therefore presents a very interesting paradigm in robotics and computer science and within this paradigm robotics researchers are heavily influenced from interdisciplinary studies typically in biology, ethology and psychology. This tutorial takes such an interdisciplinary approach and aims to present the background and theories of imitation from biology, ethology and psychology together with some of their practical implementations in robotics. The aim of tutorial is to disseminate this research field to a wider audience.

For robots to be more capable interaction partners they will necessarily need to adapt to the needs and requirements of their human companions. One way that the human could aid this adaptation may be by teaching the robot new ways of doing things by physically demonstrating different behaviours and tasks such that the robot learns new skills by imitating the learnt behaviours in appropriate contexts. In human–human teaching, the concept of scaffolding describes the process whereby the teacher guides the pupil to new competence levels by exploiting and extending existing competencies. In addition, the idea of event structuring can be used to describe how the teacher highlights important moments in an overall interaction episode. Scaffolding and event structuring robot skills in this way may be an attractive route in achieving robot adaptation; however, there are many ways in which a particular behaviour might be scaffolded or structured and the interaction process itself may have an effect on the robot's resulting performance. Our overall research goal is to understand how to design an appropriate human–robot interaction paradigm where the robot will be able to intervene and elicit knowledge from the human teacher in order to better understand the taught behaviour. In this article we examine some of these issues in two exploratory human–robot teaching scenarios. The first considers task structuring from the robot's viewpoint by varying the way in which a robot is taught. The experimental results illustrate that the way in which teaching is carried out, and primarily how the teaching steps are decomposed, has a critical effect on the efficiency of human teaching and the effectiveness of robot learning. The second experiment studies the problem from the human's viewpoint in an attempt to study the human teacher's spontaneous levels of event segmentation when analysing their own demonstrations of a routine home task to a robot. The results suggest the existence of some individual differences regarding the level of granularity spontaneously considered for the task segmentation and for those moments in the interaction which are viewed as most important.

One way in which a robotic system might be considered 'intelligent' may be an ability to learn and then generalise its learnt abilities in situations not previously encountered. Thus the ability to learn may be a prerequisite in allowing a robot to be adaptable when coping with changing environments, be useful when dealing with changing user requirements and expectations as well as being in itself a mechanism which supports the idea of intelligence and intelligent behaviour. One of the aims of the robotics research presented here is to study adaptive mechanisms which support learning with a focus on supervised learning via the interaction between a human teacher and a robot learner [6]. This interaction is based upon learning via imitation from observation of others as well as self-imitation, where the learner learns by reproducing actions it has perceived from another manipulating its body in order to highlight affordances and effectivities available for action [7]. In this talk I will suggest that the psychological notion of 'ideomotor theory' may be a good starting point from which to construct adaptable robots which support learning from imitation. In doing this I will explain the ideas behind ideomotor theory, ranging from the initial work of Lotze [3] and James [2] to more recent research by Prinz [5] where the theory is extended to support imitation. I will contrast and compare ideomotor theory with other prevailing theories of imitation, such as Active intermodal matching [4] and Associative Sequence Learning [1] and suggest that the ideomotor approach may have more to offer the roboticist in conceiving learning systems. Finally I will demonstrate how these ideas can be put into practise with some examples of scaffolded teaching used to teach physical robots an object following task.

Teaching a robot new skills may require that the teacher scaffolds the teaching experience appropriately. However, due to inherent assumptions made by a human teacher the scaffolding process may in some circumstances fail to effectively teach the robot. Here we illustrate this issue in two simple robot teaching exploratory studies and examine the assumptions made by the teacher when teaching the robot. In the first study the human teacher had to reason about robot perceived states in order to provide suitable teaching. In the second study the human teachers had to understand the perceptual constraints of the robot based on the instructions given beforehand by the experimenter and subsequently adapt the guidance given. The results suggest that although the two tasks are quite distinct in their level of complexity a common thread can be observed: people tend to underspecify their teaching. It seems that steps of the explanation are assumed to be known and skipped or not even considered at all. We reflect on the possibility that one of the major challenges in designing robots that are capable interaction partners in these teaching situations is to be able to make them communicate their internal state and current capabilities effectively. Furthermore, we also reflect on designing appropriate behavioral primitives for the robot, corresponding implications on the level of task description and for benefiting from human teaching.

Imitation is a powerful learning tool when humans and robots interact in a social context. A series of experimental runs and a small pilot user study were conducted to evaluate the performance of a system designed for robot imitation. Performance assessments of similarity of imitative behaviours were carried out by machines and by humans: the system was evaluated quantitatively (from a machine-centric perspective) and qualitatively (from a human perspective) in order to study the reconciliation of these views. The experimental results presented here illustrate how the number of exceptions can be used as a performance measure by a robotic or software imitator of an object manipulation behaviour. (In this context, exceptions are events when the optimal displacement and/or rotation that minimize the dissimilarity metrics used to generate a corresponding imitative behaviour cannot be directly achieved in the particular context.) Results of the user study giving similarity judgments on imitative behaviours were used to examine how the quantitative measure of the number of exceptions (from a robot's perspective) corresponds to the qualitative evaluation of similarity (from a human's perspective) for the imitative behaviours generated by the jabberwocky system. Results suggest that there is a good alignment between this quantitive system centered assessment and the more qualitative human-centered assessment of imitative performance.

Programming robots to carry out useful tasks is both a complex and non-trivial exercise. A simple and intuitive method to allow humans to train and shape robot behaviour is clearly a key goal in making this task easier. This paper describes an approach to this problem based on studies of social animals where two teaching strategies are applied to allow a human teacher to train a robot by moulding its actions within a carefully scaffolded environment. Within these enviroments sets of competences can be built by building stateslash action memory maps of the robot's interaction within that environment. These memory maps are then polled using a k-nearest neighbour based algorithm to provide a generalised competence. We take a novel approach in building the memory models by allowing the human teacher to construct them in a hierarchical manner. This mechanism allows a human trainer to build and extend an action-selection mechanism into which new skills can be added to the robot's repertoire of existing competencies. These techniques are implemented on physical Khepera miniature robots and validated on a variety of tasks.

An evolutionary predecessor to observational imitation may have been self-imitation. Self-imitation is where an agent is able to learn and replicate actions it has experienced through the manipulation of its body by another. This form of imitative learning has the advantage of avoiding some of the complexities encountered in observational learning such as the correspondence problem. We investigate how a system using self-imitation can be constructed with reference to psychological models of motor control including ideomotor theory and ideas from social scaffolding seen in animals to allow us to construct a robotic control system. The system allows a human trainer to teach a robot new skills and modify existing skills. Additionally the system allows the robot to notify the trainer when it is being taught skills it already possesses. We argue that this mechanism may be the first step towards the transformation from self-imitation to observational imitation. We demonstrate the system on a physical Pioneer robot with a 5-DOF arm and pan/tilt camera which is taught using self-imitation to track and point to coloured objects