Confirmation Report: Modelling Interlocutor Confusion in Situated Human Robot Interaction

Human-Robot Interaction (HRI) is an important but challenging field focused on improving the interaction between humans and robots such to make the interaction more intelligent and effective. However, building a natural conversational HRI is an interdisciplinary challenge for scholars, engineers, and designers. It is generally assumed that the pinnacle of human- robot interaction will be having fluid naturalistic conversational interaction that in important ways mimics that of how humans interact with each other. This of course is challenging at a number of levels, and in particular there are considerable difficulties when it comes to naturally monitoring and responding to the user’s mental state. On the topic of mental states, one field that has received little attention to date is moni- toring the user for possible confusion states. Confusion is a non-trivial mental state which can be seen as having at least two substates. There two confusion states can be thought of as being associated with either negative or positive emotions. In the former, when people are productively confused, they have a passion to solve any current difficulties. Meanwhile, people who are in unproductive confusion may lose their engagement and motivation to overcome those difficulties, which in turn may even lead them to drop the current conversation. While there has been some research on confusion monitoring and detection, it has been limited with the most focused on evaluating confusion states in online learning tasks. The central hypothesis of this research is that the monitoring and detection of confusion states in users is essential to fluid task-centric HRI and that it should be possible to detect such confusion and adjust policies to mitigate the confusion in users. In this report, I expand on this hypothesis and set out several research questions. I also provide a comprehensive literature review before outlining work done to date towards my research hypothesis, I also set out plans for future experimental work

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

Embodied language learning and cognitive bootstrapping: methods and design principles

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

Embodied Language Learning and Cognitive Bootstrapping:Methods and Design Principles

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

Temporal Patterns in Multi-modal Social Interaction between Elderly Users and Service Robot

Social interaction, especially for older people living alone is a challenge currently facing human-robot interaction (HRI). User interfaces to manage service robots in home environments need to be tailored for older people. Multi-modal interfaces providing users with more than one communication option seem promising. There has been little research on user preference towards HRI interfaces; most studies have focused on utility and functionality of the interface. In this paper, we took both objective observations and participants’ opinions into account in studying older users with a robot partner. Our study was under the framework of the EU FP7 Robot-Era Project. The developed dual-modal robot interface offered older users options of speech or touch screen to perform tasks. Fifteen people aged from 70 to 89 years old, participated. We analyzed the spontaneous actions of the participants, including their attentional activities (eye contacts) and conversational activities, the temporal characteristics (timestamps, duration of events, event transitions) of these social behaviours, as well as questionnaires. This combination of data distinguishes it from other studies that focused on questionnaire ratings only. There were three main findings. First, the design of the Robot-Era interface was very acceptable for older users. Secondly, most older people used both speech and tablet to perform the food delivery service, with no difference in their preferences towards either. Thirdly, these older people had frequent and long-duration eye contact with the robot during their conversations, showing patience when expecting the robot to respond. They enjoyed the service. Overall, social engagement with the robot demonstrated by older people was no different from what might be expected towards a human partner. This study is an early attempt to reveal the social connections between human beings and a personal robot in real life. Our observations and findings should inspire new insights in HRI research and eventually contribute to next-generation intelligent robot developmen

Pragmatic Frames for Teaching and Learning in Human-Robot interaction: Review and Challenges

Vollmer A-L, Wrede B, Rohlfing KJ, Oudeyer P-Y. Pragmatic Frames for Teaching and Learning in Human-Robot interaction: Review and Challenges. FRONTIERS IN NEUROROBOTICS. 2016;10: 10.One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning-teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human-human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; (2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching

Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations

People often encounter difficulties in building shared understanding during everyday conversation. The most common symptom of these difficulties are self-repairs, when a speaker restarts, edits or amends their utterances mid-turn. Previous work has focused on the verbal signals of self-repair, i.e. speech disfluences (filled pauses, truncated words and phrases, word substitutions or reformulations), and computational tools now exist that can automatically detect these verbal phenomena. However, face-to-face conversation also exploits rich non-verbal resources and previous research suggests that self-repairs are associated with distinct hand movement patterns. This paper extends those results by exploring head and hand movements of both speakers and listeners using two motion parameters: height (vertical position) and 3D velocity. The results show that speech sequences containing self-repairs are distinguishable from fluent ones: speakers raise their hands and head more (and move more rapidly) during self-repairs. We obtain these results by analysing data from a corpus of 13 unscripted dialogues, and we discuss how these findings could support the creation of improved cognitive artificial systems for natural human-machine and human-robot interaction

Attentional mechanisms for socially interactive robots – a survey

This review intends to provide an overview of the state of the art in the modeling and implementation of automatic attentional mechanisms for socially interactive robots. Humans assess and exhibit intentionality by resorting to multisensory processes that are deeply rooted within low-level automatic attention-related mechanisms of the brain. For robots to engage with humans properly, they should also be equipped with similar capabilities. Joint attention, the precursor of many fundamental types of social interactions, has been an important focus of research in the past decade and a half, therefore providing the perfect backdrop for assessing the current status of state-of-the-art automatic attentional-based solutions. Consequently, we propose to review the influence of these mechanisms in the context of social interaction in cutting-edge research work on joint attention. This will be achieved by summarizing the contributions already made in these matters in robotic cognitive systems research, by identifying the main scientific issues to be addressed by these contributions and analyzing how successful they have been in this respect, and by consequently drawing conclusions that may suggest a roadmap for future successful research efforts

Shared Perception in Human-Robot Interaction

Interaction can be seen as a composition of perspectives: the integration of perceptions, intentions, and actions on the environment two or more agents share. For an interaction to be effective, each agent must be prone to “sharedness”: being situated in a common environment, able to read what others express about their perspective, and ready to adjust one’s own perspective accordingly. In this sense, effective interaction is supported by perceiving the environment jointly with others, a capability that in this research is called Shared Perception. Nonetheless, perception is a complex process that brings the observer receiving sensory inputs from the external world and interpreting them based on its own, previous experiences, predictions, and intentions. In addition, social interaction itself contributes to shaping what is perceived: others’ attention, perspective, actions, and internal states may also be incorporated into perception. Thus, Shared perception reflects the observer's ability to integrate these three sources of information: the environment, the self, and other agents. If Shared Perception is essential among humans, it is equally crucial for interaction with robots, which need social and cognitive abilities to interact with humans naturally and successfully. This research deals with Shared Perception within the context of Social Human-Robot Interaction (HRI) and involves an interdisciplinary approach. The two general axes of the thesis are the investigation of human perception while interacting with robots and the modeling of robot’s perception while interacting with humans. Such two directions are outlined through three specific Research Objectives, whose achievements represent the contribution of this work. i) The formulation of a theoretical framework of Shared Perception in HRI valid for interpreting and developing different socio-perceptual mechanisms and abilities. ii) The investigation of Shared Perception in humans focusing on the perceptual mechanism of Context Dependency, and therefore exploring how social interaction affects the use of previous experience in human spatial perception. iii) The implementation of a deep-learning model for Addressee Estimation to foster robots’ socio-perceptual skills through the awareness of others’ behavior, as suggested in the Shared Perception framework. To achieve the first Research Objective, several human socio-perceptual mechanisms are presented and interpreted in a unified account. This exposition parallels mechanisms elicited by interaction with humans and humanoid robots and aims to build a framework valid to investigate human perception in the context of HRI. Based on the thought of D. Davidson and conceived as the integration of information coming from the environment, the self, and other agents, the idea of "triangulation" expresses the critical dynamics of Shared Perception. Also, it is proposed as the functional structure to support the implementation of socio-perceptual skills in robots. This general framework serves as a reference to fulfill the other two Research Objectives, which explore specific aspects of Shared Perception. For what concerns the second Research Objective, the human perceptual mechanism of Context Dependency is investigated, for the first time, within social interaction. Human perception is based on unconscious inference, where sensory inputs integrate with prior information. This phenomenon helps in facing the uncertainty of the external world with predictions built upon previous experience. To investigate the effect of social interaction on such a mechanism, the iCub robot has been used as an experimental tool to create an interactive scenario with a controlled setting. A user study based on psychophysical methods, Bayesian modeling, and a neural network analysis of human results demonstrated that social interaction influenced Context Dependency so that when interacting with a social agent, humans rely less on their internal models and more on external stimuli. Such results are framed in Shared Perception and contribute to revealing the integration dynamics of the three sources of Shared Perception. The others’ presence and social behavior (other agents) affect the balance between sensory inputs (environment) and personal history (self) in favor of the information shared with others, that is, the environment. The third Research Objective consists of tackling the Addressee Estimation problem, i.e., understanding to whom a speaker is talking, to improve the iCub social behavior in multi-party interactions. Addressee Estimation can be considered a Shared Perception ability because it is achieved by using sensory information from the environment, internal representations of the agents’ position, and, more importantly, the understanding of others’ behavior. An architecture for Addressee Estimation is thus designed considering the integration process of Shared Perception (environment, self, other agents) and partially implemented with respect to the third element: the awareness of others’ behavior. To achieve this, a hybrid deep-learning (CNN+LSTM) model is developed to estimate the speaker-robot relative placement of the addressee based on the non-verbal behavior of the speaker. Addressee Estimation abilities based on Shared Perception dynamics are aimed at improving multi-party HRI. Making robots aware of other agents’ behavior towards the environment is the first crucial step for incorporating such information into the robot’s perception and modeling Shared Perception