RISE: an open-source architecture for interdisciplinary and reproducible human-robot interaction research.

In this article, we present RISE-a Robotics Integration and Scenario-Management Extensible-Architecture-for designing human-robot dialogs and conducting Human-Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.

Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue.

Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts-measured as breaks in the finger movement data before the correct gesture was carried out-when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.

On the potentials of interaction breakdowns for HRI.

How do we switch between "playing along" and treating robots as technical agents? We propose interaction breakdowns to help solve this "social artifact puzzle": Breaks cause changes from fluid interaction to explicit reasoning and interaction with the raw artifact. These changes are closely linked to understanding the technical architecture and could be used to design better human-robot interaction (HRI).

[The COVID-19 Pandemic as an Opportunity for Teletherapy? - A Survey of non-medical therapy professionals in the health sector]. / Die COVID-19-Pandemie als Chance für Teletherapie? ­ Eine Umfrage bei Vertreter*innen von Gesundheitsfachberufen.

OBJECTIVE: The aim of the study was to investigate the use of teletherapy during the corona pandemic by three non-medical therapy professionals in the health sector. METHOD: As part of a questionnaire-based online survey, 282 participants from the field of ergotherapy, physiotherapy and speech therapy were asked about usage behavior, challenges, potentials, and general conditions of teletherapy. RESULTS: Especially ergo and speech therapists used teletherapy during the corona pandemic. From their point of view, teletherapy also had a potential to be used as an alternative form of therapy, regardless of the coronavirus pandemic, adding that there was a great need for further assistance and training in the field of teletherapy. CONCLUSION: To implement this form of therapy on a long-term basis, in addition to technical requirements and training opportunities, accounting formalities need to be clarified.

A Multi-Directional and Agile Academic Knowledge Transfer Strategy for Healthcare Technology.

Technology, especially cognitive agents and robots, has significant potential to improve the healthcare system and patient care. However, innovation within academia seldomly finds its way into practice. At least in Germany, there is still a digitalization gap between academia and healthcare practice and little understanding of how healthcare facilities can successfully purchase, implement, and adopt new knowledge and technology. Therefore, the aim of this study is to develop a successful academic knowledge transfer strategy for healthcare technology. We conducted a qualitative study with academic staff working in higher education in Germany and professionals in their practice partner organizations. In 15 semi-structured interviews, we aimed to assess interviewees experiences with knowledge transfer, to identify perceived influencing factors, and to understand the key aspects of a successful knowledge transfer strategy. The Dynamic Knowledge Transfer Model by Wehn and Montalvo, 2018 was used for data analysis. Based on our findings, we suggest that a successful transfer strategy between academia and practice needs to be multi-directional and agile. Moreover, partners within the transfer need to be on equal terms about expected knowledge transfer project outcomes. Our proposed measures focus particularly on regular consultations and communication during and after the project proposal phase.

AlignTool: The automatic temporal alignment of spoken utterances in German, Dutch, and British English for psycholinguistic purposes.

In language production research, the latency with which speakers produce a spoken response to a stimulus and the onset and offset times of words in longer utterances are key dependent variables. Measuring these variables automatically often yields partially incorrect results. However, exact measurements through the visual inspection of the recordings are extremely time-consuming. We present AlignTool, an open-source alignment tool that establishes preliminarily the onset and offset times of words and phonemes in spoken utterances using Praat, and subsequently performs a forced alignment of the spoken utterances and their orthographic transcriptions in the automatic speech recognition system MAUS. AlignTool creates a Praat TextGrid file for inspection and manual correction by the user, if necessary. We evaluated AlignTool's performance with recordings of single-word and four-word utterances as well as semi-spontaneous speech. AlignTool performs well with audio signals with an excellent signal-to-noise ratio, requiring virtually no corrections. For audio signals of lesser quality, AlignTool still is highly functional but its results may require more frequent manual corrections. We also found that audio recordings including long silent intervals tended to pose greater difficulties for AlignTool than recordings filled with speech, which AlignTool analyzed well overall. We expect that by semi-automatizing the temporal analysis of complex utterances, AlignTool will open new avenues in language production research.

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

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.

An Alternative to Mapping a Word onto a Concept in Language Acquisition: Pragmatic Frames.

The classic mapping metaphor posits that children learn a word by mapping it onto a concept of an object or event. However, we believe that a mapping metaphor cannot account for word learning, because even though children focus attention on objects, they do not necessarily remember the connection between the word and the referent unless it is framed pragmatically, that is, within a task. Our theoretical paper proposes an alternative mechanism for word learning. Our main premise is that word learning occurs as children accomplish a goal in cooperation with a partner. We follow Bruner's (1983) idea and further specify pragmatic frames as the learning units that drive language acquisition and cognitive development. These units consist of a sequence of actions and verbal behaviors that are co-constructed with a partner to achieve a joint goal. We elaborate on this alternative, offer some initial parametrizations of the concept, and embed it in current language learning approaches.

The ITALK project: a developmental robotics approach to the study of individual, social, and linguistic learning.

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.

Robots show us how to teach them: feedback from robots shapes tutoring behavior during action learning.

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.

Can machines think? Interaction and perspective taking with robots investigated via fMRI.

BACKGROUND: When our PC goes on strike again we tend to curse it as if it were a human being. Why and under which circumstances do we attribute human-like properties to machines? Although humans increasingly interact directly with machines it remains unclear whether humans implicitly attribute intentions to them and, if so, whether such interactions resemble human-human interactions on a neural level. In social cognitive neuroscience the ability to attribute intentions and desires to others is being referred to as having a Theory of Mind (ToM). With the present study we investigated whether an increase of human-likeness of interaction partners modulates the participants' ToM associated cortical activity. METHODOLOGY/PRINCIPAL FINDINGS: By means of functional magnetic resonance imaging (subjects n = 20) we investigated cortical activity modulation during highly interactive human-robot game. Increasing degrees of human-likeness for the game partner were introduced by means of a computer partner, a functional robot, an anthropomorphic robot and a human partner. The classical iterated prisoner's dilemma game was applied as experimental task which allowed for an implicit detection of ToM associated cortical activity. During the experiment participants always played against a random sequence unknowingly to them. Irrespective of the surmised interaction partners' responses participants indicated having experienced more fun and competition in the interaction with increasing human-like features of their partners. Parametric modulation of the functional imaging data revealed a highly significant linear increase of cortical activity in the medial frontal cortex as well as in the right temporo-parietal junction in correspondence with the increase of human-likeness of the interaction partner (computer