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Spatial Referring Expressions in Child-Robot Interaction: Let's Be Ambiguous!
Abstract and Figures
Establishing common ground when attempting to disambiguate spatial locations is difficult at the best of times, but is even more challenging between children and robots. Here, we present a study that examined how 94 children (aged 5-8) communicate spatial locations to other children, adults and robots in face-to-face interactions. While standard HRI implementations focus on non-ambiguous statements, we found this only comprised about 20% of children's task based utterances. Rather, they rely on brief, iterative, repair statements to communicate about spatial locations. Our observations offer strong experimental evidence to inform future dialogue systems for robots interacting with children.
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Generating spatial referring expressions is key to allowing robots to communicate with people in an environment. The focus of most algorithms for generation is to create a non-ambiguous description, and how best to deal with the combination explosion this can create in a complex environment. However, this is not how people naturally communicate. Humans tend to give an under-specified description and then rely on a strategy of repair to reduce the number of possible locations or objects until the correct one is identified, what we refer to here as a dynamic description. We present here a method for generating these dynamic descriptions for Human Robot Interaction, using machine learning to generate repair statements. We also present a study with 61 participants in a task on object placement. This task was presented in a 2D environment that favored a non-ambiguous description. In this study we demonstrate that our dynamic method of communication can be more efficient for people to identify a location compared to one that is non-ambiguous.
In recent years, it has been suggested that social robots have potential as tutors and educators for both children and adults. While robots have been shown to be effective in teaching knowledge and skill-based topics, we wish to explore how social robots can be used to tutor a second language to young children. As language learning relies on situated, grounded and social learning, in which interaction and repeated practice are central, social robots hold promise as educational tools for supporting second language learning. This paper surveys the developmental psychology of second language learning and suggests an agenda to study how core concepts of second language learning can be taught by a social robot. It suggests guidelines for designing robot tutors based on observations of second language learning in human–human scenarios, various technical aspects and early studies regarding the effectiveness of social robots as second language tutors.
This study aimed to examine the effect of robot assisted language learning (RALL) on the anxiety level and
attitude in English vocabulary acquisition amongst Iranian EFL junior high school students. Forty-six female students, who were beginners at the age of 12, participated in this study and were randomly assigned into two groups of RALL (30 students) and non-RALL (16 students). The textbook, the materials, as well as the teacher were the same in the two groups. However in theRALLgroup, the treatmentwas given by a teacher accompanied by a humanoid robot assistant.Two questionnaires of anxiety and attitude were utilized to measure the students’ anxiety and attitude (Horwitz et al. 1986; Alemi and Alipour 2012). The results of descriptive and ttests indicated that there was lower anxiety and a more positive attitude towards English vocabulary acquisition in the RALL group compared with those in the non-RALL group. The study showed that the students in the RALL group had great fun in the learning process; they also believed theywere
learning more effectively, which helped them boost their motivation in the long run. The present study provides new insights into the use of technology in the language classroom, suggesting that teachers and material developers should integrate technology into the language learning process.
As robots collaborate with humans in increasingly diverse environ-ments, they will need to effectively refer to objects of joint inter-est and adapt their references to various physical, environmental, and task conditions. Humans use a broad range of deictic ges-tures—gestures that direct attention to collocated objects, persons, or spaces—that include pointing, touching, and exhibiting to help their listeners understand their references. These gestures offer varying levels of support under different conditions, making some gestures more or less suitable for different settings. While these ges-tures offer a rich space for designing communicative behaviors for robots, a better understanding of how different deictic gestures affect communication under different conditions is critical for achieving effective human-robot interaction. In this paper, we seek to build such an understanding by implementing six deictic gestures on a hu-manlike robot and evaluating their communicative effectiveness in six diverse settings that represent physical, environmental, and task conditions under which robots are expected to employ deictic com-munication. Our results show that gestures which come into physical contact with the object offer the highest overall communicative ac-curacy and that specific settings benefit from the use of particular types of gestures. Our results highlight the rich design space for deictic gestures and inform how robots might adapt their gestures to the specific physical, environmental, and task conditions.
Specifically, we wish to invite contributions viewing language and cognition as linking what goes on in the head with causal processes that are intersubjective, multimodal, affect-laden, and organised by historically rooted customs and artefacts.… The purpose of the workshop is not so much to present completed work as to find new ways of tackling a complex issue and to launch collaboration among participants to that end.… Since the workshop focuses on how symbol grounding can be reconsidered when language is viewed as a dynamical process rooted in both culture and biology, research related to robotic or computer modelling of symbol grounding, psychological and linguistic viewpoints on cognitive development and semiotic dynamics are of great interest.
Pickering and Garrod (2004) argued that alignment is the basis of successful communication in dialogue. In other words, successful
communication goes hand-in-hand with the development of similar representations in the interlocutors. But what exactly does
this mean? In this paper, we attempt to define alignment, contrasting alignment of situation models with alignment of linguistic
representations. We then speculate on how these notions are related and why they lead to conversational success
For people to contribute to discourse, they must do more than utter the right sentence at the right time. The basic requirement is that they add to their common ground in an orderly way. To do this, we argue, they try to establish for each utterance the mutual belief that the addressees have understood what the speaker meant well enough for current purposes. This is accomplished by the collective actions of the current contributor and his or her partners, and these result in units of conversation called contributions. We present a model of contributions and show how it accounts for a variety of features of everyday conversations.
In conversation, speakers and addressees work together in the making of a definite reference. In the model we propose, the speaker initiates the process by presenting or inviting a noun phrase. Before going on to the next contribution, the participants, if necessary, repair, expand on, or replace the noun phrase in an iterative process until they reach a version they mutually accept. In doing so they try to minimize their joint effort. The preferred procedure is for the speaker to present a simple noun phrase and for the addressee to accept it by allowing the next contribution to begin. We describe a communication task in which pairs of people conversed about arranging complex figures and show how the proposed model accounts for many features of the references they produced. The model follows, we suggest, from the mutual responsibility that participants in conversation bear toward the understanding of each utterance.
We examine the problem of generating definite noun phrases that are appropriate referring expressions; i.e, noun phrases that (1) successfully identify the intended referent to the hearer whilst (2) not conveying to her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system. This paper will appear in the the April--June 1995 issue of Cognitive Science, and is made available on cmp-lg with the permission of Ablex, the publishers of that journal.
A touchscreen-based'sandtray'to facilitate, mediate and contextualise human-robot social interaction
- P Baxter
- R Wood
- T Belpaeme
P. Baxter, R. Wood, and T. Belpaeme. A touchscreen-based'sandtray'to facilitate,
mediate and contextualise human-robot social interaction. In Proceedings of the
seventh annual ACM/IEEE international conference on Human-Robot
Interaction, pages 105-106. ACM, 2012.