@article{17182, abstract = {{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.}}, author = {{Lyon, Caroline and Nehaniv, Chrystopher L. and Saunders, Joe and Belpaeme, Tony and Bisio, Ambra and Fischer, Kerstin and Forster, Frank and Lehmann, Hagen and Metta, Giorgio and Mohan, Vishwanathan and Morse, Anthony and Nolfi, Stefano and Nori, Francesco and Rohlfing, Katharina and Sciutti, Alessandra and Tani, Jun and Tuci, Elio and Wrede, Britta and Zeschel, Arne and Cangelosi, Angelo}}, issn = {{1729-8814}}, journal = {{International Journal of Advanced Robotic Systems}}, keywords = {{Robot Language, Human Robot Interaction, HRI, Developmental Robotics, Cognitive Bootstrapping, Statistical Learning}}, number = {{3}}, publisher = {{Intech Europe}}, title = {{{Embodied Language Learning and Cognitive Bootstrapping: Methods and Design Principles}}}, doi = {{10.5772/63462}}, volume = {{13}}, year = {{2016}}, } @article{17189, abstract = {{Alignment is a phenomenon observed in human conversation: Dialog partners' behavior converges in many respects. Such alignment has been proposed to be automatic and the basis for communicating successfully. Recent research on human-computer dialog promotes a mediated communicative design account of alignment according to which the extent of alignment is influenced by interlocutors' beliefs about each other. Our work aims at adding to these findings in two ways. (a) Our work investigates alignment of manual actions, instead of lexical choice. (b) Participants interact with the iCub humanoid robot, instead of an artificial computer dialog system. Our results confirm that alignment also takes place in the domain of actions. We were not able to replicate the results of the original study in general in this setting, but in accordance with its findings, participants with a high questionnaire score for emotional stability and participants who are familiar with robots align their actions more to a robot they believe to be basic than to one they believe to be advanced. Regarding alignment over the course of an interaction, the extent of alignment seems to remain constant, when participants believe the robot to be advanced, but it increases over time, when participants believe the robot to be a basic version.}}, author = {{Vollmer, Anna-Lisa and Rohlfing, Katharina and Wrede, Britta and Cangelosi, Angelo}}, issn = {{1875-4791}}, journal = {{International Journal of Social Robotics}}, keywords = {{learning, Human-robot interaction, Alignment, Robot social, Action understanding}}, number = {{2}}, pages = {{241--252}}, publisher = {{Springer-Verlag}}, title = {{{Alignment to the Actions of a Robot}}}, doi = {{10.1007/s12369-014-0252-0}}, volume = {{7}}, year = {{2015}}, } @article{17192, abstract = {{In order for artificial intelligent systems to interact naturally with human users, they need to be able to learn from human instructions when actions should be imitated. Human tutoring will typically consist of action demonstrations accompanied by speech. In the following, the characteristics of human tutoring during action demonstration will be examined. A special focus will be put on the distinction between two kinds of motion events: path-oriented actions and manner-oriented actions. Such a distinction is inspired by the literature pertaining to cognitive linguistics, which indicates that the human conceptual system can distinguish these two distinct types of motion. These two kinds of actions are described in language by more path-oriented or more manner-oriented utterances. In path-oriented utterances, the source, trajectory, or goal is emphasized, whereas in manner-oriented utterances the medium, velocity, or means of motion are highlighted. We examined a video corpus of adult-child interactions comprised of three age groups of children-pre-lexical, early lexical, and lexical-and two different tasks, one emphasizing manner more strongly and one emphasizing path more strongly. We analyzed the language and motion of the caregiver and the gazing behavior of the child to highlight the differences between the tutoring and the acquisition of the manner and path concepts. The results suggest that age is an important factor in the development of these action categories. The analysis of this corpus has also been exploited to develop an intelligent robotic behavior -the tutoring spotter system-able to emulate children's behaviors in a tutoring situation, with the aim of evoking in human subjects a natural and effective behavior in teaching to a robot. The findings related to the development of manner and path concepts have been used to implement new effective feedback strategies in the tutoring spotter system, which should provide improvements in human-robot interaction.}}, author = {{Lohan, Katrin S. and Griffiths, Sascha and Sciutti, Alessandra and Partmann, Tim C. and Rohlfing, Katharina}}, issn = {{1756-8757}}, journal = {{Topics in Cognitive Science}}, keywords = {{Imitation, Tutoring, Adult-child interaction, Human-robot interaction, Semantics, Teachable robots}}, number = {{3}}, pages = {{492--512}}, publisher = {{Wiley-Blackwell}}, title = {{{Co-development of manner and path concepts in language, action, and eye-gaze behavior}}}, doi = {{10.1111/tops.12098}}, volume = {{6}}, year = {{2014}}, } @article{17225, abstract = {{How is communicative gesture behavior in robots perceived by humans? Although gesture is crucial in social interaction, this research question is still largely unexplored in the field of social robotics. Thus, the main objective of the present work is to investigate how gestural machine behaviors can be used to design more natural communication in social robots. The chosen approach is twofold. Firstly, the technical challenges encountered when implementing a speech-gesture generation model on a robotic platform are tackled. We present a framework that enables the humanoid robot to flexibly produce synthetic speech and co-verbal hand and arm gestures at run-time, while not being limited to a predefined repertoire of motor actions. Secondly, the achieved flexibility in robot gesture is exploited in controlled experiments. To gain a deeper understanding of how communicative robot gesture might impact and shape human perception and evaluation of human-robot interaction, we conducted a between-subjects experimental study using the humanoid robot in a joint task scenario. We manipulated the non-verbal behaviors of the robot in three experimental conditions, so that it would refer to objects by utilizing either (1) unimodal (i.e., speech only) utterances, (2) congruent multimodal (i.e., semantically matching speech and gesture) or (3) incongruent multimodal (i.e., semantically non-matching speech and gesture) utterances. Our findings reveal that the robot is evaluated more positively when non-verbal behaviors such as hand and arm gestures are displayed along with speech, even if they do not semantically match the spoken utterance.}}, author = {{Salem, Maha and Kopp, Stefan and Wachsmuth, Ipke and Rohlfing, Katharina and Joublin, Frank}}, issn = {{1875-4805}}, journal = {{International Journal of Social Robotics, Special Issue on Expectations, Intentions, and Actions}}, keywords = {{Social Human-Robot Interaction, Multimodal Interaction and Conversational Skills, Robot Companions and Social Robots, Non-verbal Cues and Expressiveness}}, number = {{2}}, pages = {{201--217}}, publisher = {{Springer Science + Business Media}}, title = {{{Generation and evaluation of communicative robot gesture}}}, doi = {{10.1007/s12369-011-0124-9}}, volume = {{4}}, year = {{2012}}, } @article{17428, abstract = {{How is communicative gesture behavior in robots perceived by humans? Although gesture is crucial in social interaction, this research question is still largely unexplored in the field of social robotics. Thus, the main objective of the present work is to investigate how gestural machine behaviors can be used to design more natural communication in social robots. The chosen approach is twofold. Firstly, the technical challenges encountered when implementing a speech-gesture generation model on a robotic platform are tackled. We present a framework that enables the humanoid robot to flexibly produce synthetic speech and co-verbal hand and arm gestures at run-time, while not being limited to a predefined repertoire of motor actions. Secondly, the achieved flexibility in robot gesture is exploited in controlled experiments. To gain a deeper understanding of how communicative robot gesture might impact and shape human perception and evaluation of human-robot interaction, we conducted a between-subjects experimental study using the humanoid robot in a joint task scenario. We manipulated the non-verbal behaviors of the robot in three experimental conditions, so that it would refer to objects by utilizing either (1) unimodal (i.e., speech only) utterances, (2) congruent multimodal (i.e., semantically matching speech and gesture) or (3) incongruent multimodal (i.e., semantically non-matching speech and gesture) utterances. Our findings reveal that the robot is evaluated more positively when non-verbal behaviors such as hand and arm gestures are displayed along with speech, even if they do not semantically match the spoken utterance.}}, author = {{Salem, Maha and Kopp, Stefan and Wachsmuth, Ipke and Rohlfing, Katharina and Joublin, Frank}}, issn = {{1875-4805}}, journal = {{International Journal of Social Robotics, Special Issue on Expectations, Intentions, and Actions}}, keywords = {{Social Human-Robot Interaction, Multimodal Interaction and Conversational Skills, Robot Companions and Social Robots, Non-verbal Cues and Expressiveness}}, number = {{2}}, pages = {{201--217}}, publisher = {{Springer Science + Business Media}}, title = {{{Generation and evaluation of communicative robot gesture}}}, doi = {{10.1007/s12369-011-0124-9}}, volume = {{4}}, year = {{2012}}, } @article{17233, abstract = {{It has been proposed that the design of robots might benefit from interactions that are similar to caregiver–child interactions, which is tailored to children’s respective capacities to a high degree. However, so far little is known about how people adapt their tutoring behaviour to robots and whether robots can evoke input that is similar to child-directed interaction. The paper presents detailed analyses of speakers’ linguistic and non-linguistic behaviour, such as action demonstration, in two comparable situations: In one experiment, parents described and explained to their nonverbal infants the use of certain everyday objects; in the other experiment, participants tutored a simulated robot on the same objects. The results, which show considerable differences between the two situations on almost all measures, are discussed in the light of the computer-as-social-actor paradigm and the register hypothesis.}}, author = {{Fischer, Kerstin and Foth, Kilian and Rohlfing, Katharina and Wrede, Britta}}, issn = {{1572-0381}}, journal = {{Interaction Studies}}, keywords = {{human–robot interaction (HRI), social communication, register theory, motionese, robotese, child-directed speech (CDS), motherese, mindless transfer, computers-as-social-actors}}, number = {{1}}, pages = {{134--161}}, publisher = {{John Benjamins Publishing Company}}, title = {{{Mindful tutors: Linguistic choice and action demonstration in speech to infants and a simulated robot}}}, doi = {{10.1075/is.12.1.06fis}}, volume = {{12}}, year = {{2011}}, } @inproceedings{17244, abstract = {{Robots interacting with humans need to understand actions and make use of language in social interactions. Research on infant development has shown that language helps the learner to structure visual observations of action. This acoustic information typically in the form of narration overlaps with action sequences and provides infants with a bottom-up guide to find structure within them. This concept has been introduced as acoustic packaging by Hirsh-Pasek and Golinkoff. We developed and integrated a prominence detection module in our acoustic packaging system to detect semantically relevant information linguistically highlighted by the tutor. Evaluation results on speech data from adult-infant interactions show a significant agreement with human raters. Furthermore a first approach based on acoustic packages which uses the prominence detection results to generate acoustic feedback is presented. Index Terms: prominence, multimodal action segmentation, human robot interaction, feedback}}, author = {{Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede, Britta and Rohlfing, Katharina}}, booktitle = {{Interspeech 2011 (12th Annual Conference of the International Speech Communication Association)}}, keywords = {{Feedback, Human Robot Interaction, Prominence, Multimodal Action Segmentation}}, pages = {{3105--3108}}, title = {{{Using Prominence Detection to Generate Acoustic Feedback in Tutoring Scenarios}}}, year = {{2011}}, } @inproceedings{17245, author = {{Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede, Britta and Rohlfing, Katharina}}, issn = {{1662-5188}}, keywords = {{Prominence, Multimodal Action Segmentation, Feedback, Color Saliency, Human Robot Interaction}}, title = {{{Acoustic Packaging and the Learning of Words}}}, doi = {{10.3389/conf.fncom.2011.52.00020}}, year = {{2011}}, } @inproceedings{17272, abstract = {{In developmental research, tutoring behavior has been identified as scaffolding infants' learning processes. It has been defined in terms of child-directed speech (Motherese), child-directed motion (Motionese), and contingency. In the field of developmental robotics, research often assumes that in human-robot interaction (HRI), robots are treated similar to infants, because their immature cognitive capabilities benefit from this behavior. However, according to our knowledge, it has barely been studied whether this is true and how exactly humans alter their behavior towards a robotic interaction partner. In this paper, we present results concerning the acceptance of a robotic agent in a social learning scenario obtained via comparison to adults and 8-11 months old infants in equal conditions. These results constitute an important empirical basis for making use of tutoring behavior in social robotics. In our study, we performed a detailed multimodal analysis of HRI in a tutoring situation using the example of a robot simulation equipped with a bottom-up saliency-based attention model. Our results reveal significant differences in hand movement velocity, motion pauses, range of motion, and eye gaze suggesting that for example adults decrease their hand movement velocity in an Adult-Child Interaction (ACI), opposed to an Adult-Adult Interaction (AAI) and this decrease is even higher in the Adult-Robot Interaction (ARI). We also found important differences between ACI and ARI in how the behavior is modified over time as the interaction unfolds. These findings indicate the necessity of integrating top-down feedback structures into a bottom-up system for robots to be fully accepted as interaction partners.}}, author = {{Vollmer, Anna-Lisa and Lohan, Katrin Solveig and Fischer, Kerstin and Nagai, Yukie and Pitsch, Karola and Fritsch, Jannik and Rohlfing, Katharina and Wrede, Britta}}, booktitle = {{Development and Learning, 2009. ICDL 2009. IEEE 8th International Conference on Development and Learning}}, keywords = {{robot simulation, hand movement velocity, robotic interaction partner, robotic agent, robot-directed interaction, multimodal analysis, Motionese, Motherese, intelligent tutoring systems, immature cognitive capability, human computer interaction, eye gaze, child-directed speech, child-directed motion, bottom-up system, bottom-up saliency-based attention model, adult-robot interaction, adult-child interaction, adult-adult interaction, human-robot interaction, action learning, social learning scenario, social robotics, software agents, top-down feedback structures, tutoring behavior}}, pages = {{1--6}}, publisher = {{IEEE}}, title = {{{People modify their tutoring behavior in robot-directed interaction for action learning}}}, doi = {{10.1109/DEVLRN.2009.5175516}}, year = {{2009}}, } @inproceedings{17267, author = {{Lohse, Manja and Hanheide, Marc and Rohlfing, Katharina and Sagerer, Gerhard}}, booktitle = {{Proceedings of the 4th ACM/IEEE international conference on Human robot interaction - HRI '09}}, keywords = {{SINA, human robot interaction, biron}}, pages = {{93--100}}, title = {{{Systemic interaction analysis (SInA) in HRI}}}, doi = {{10.1145/1514095.1514114}}, year = {{2009}}, } @inproceedings{17278, abstract = {{This paper investigates the influence of feedback provided by an autonomous robot (BIRON) on users’ discursive behavior. A user study is described during which users show objects to the robot. The results of the experiment indicate, that the robot’s verbal feedback utterances cause the humans to adapt their own way of speaking. The changes in users’ verbal behavior are due to their beliefs about the robots knowledge and abilities. In this paper they are identified and grouped. Moreover, the data implies variations in user behavior regarding gestures. Unlike speech, the robot was not able to give feedback with gestures. Due to the lack of feedback, users did not seem to have a consistent mental representation of the robot’s abilities to recognize gestures. As a result, changes between different gestures are interpreted to be unconscious variations accompanying speech.}}, author = {{Lohse, Manja and Rohlfing, Katharina and Wrede, Britta and Sagerer, Gerhard}}, isbn = {{1050-4729}}, keywords = {{discursive behavior, autonomous robot, BIRON, man-machine systems, robot abilities, robot knowledge, user gestures, robot verbal feedback utterance, speech processing, user verbal behavior, service robots, human-robot interaction, human computer interaction, gesture recognition}}, pages = {{3481--3486}}, title = {{{“Try something else!” — When users change their discursive behavior in human-robot interaction}}}, doi = {{10.1109/ROBOT.2008.4543743}}, year = {{2008}}, }