The Hesitating Robot - Implementation and First Impressions

Carlmeyer, Birte; Betz, Simon; Wagner, Petra; Wrede, Britta; Schlangen, David

doi:10.1145/3173386.3176992

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…Pepper's hesitations were predefined and generated during the normal synthesis process with Pepper's ALAnimatedSpeech interface. As a hesitation strategy, we adapted the hesitation strategy for synthetic speech proposed by Betz et al [47], which has already been tested in an HRI scenario [10]. To make sure the hesitations are recognized as hesitations and not as a normal break, we decided to use an additional silent pause of 1500 ms before the filler and 1000 ms after it.…”

Section: Hesitationsmentioning

confidence: 99%

“…Here, we focus on hesitations as a specific scaffolding strategy-a measure to support learners in acquiring new knowledge and skills. Our previous investigations have focused on the benefits of hesitations based on behavioral data such as correctly memorized information or correctly oriented gaze [9,10]. However, the previous approach focused on interaction results but did not shed light on the processes that actually lead to better memorization.…”

Section: Introductionmentioning

confidence: 99%

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EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

Richter

Putze

Ivucic

et al. 2023

MTI

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In this paper, we investigate the effect of distractions and hesitations as a scaffolding strategy. Recent research points to the potential beneficial effects of a speaker’s hesitations on the listeners’ comprehension of utterances, although results from studies on this issue indicate that humans do not make strategic use of them. The role of hesitations and their communicative function in human-human interaction is a much-discussed topic in current research. To better understand the underlying cognitive processes, we developed a human–robot interaction (HRI) setup that allows the measurement of the electroencephalogram (EEG) signals of a human participant while interacting with a robot. We thereby address the research question of whether we find effects on single-trial EEG based on the distraction and the corresponding robot’s hesitation scaffolding strategy. To carry out the experiments, we leverage our LabLinking method, which enables interdisciplinary joint research between remote labs. This study could not have been conducted without LabLinking, as the two involved labs needed to combine their individual expertise and equipment to achieve the goal together. The results of our study indicate that the EEG correlates in the distracted condition are different from the baseline condition without distractions. Furthermore, we could differentiate the EEG correlates of distraction with and without a hesitation scaffolding strategy. This proof-of-concept study shows that LabLinking makes it possible to conduct collaborative HRI studies in remote laboratories and lays the first foundation for more in-depth research into robotic scaffolding strategies.

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Section: Hesitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

Richter

Putze

Ivucic

et al. 2023

MTI

Self Cite

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“…Similarly, robots can demostrate how they understand a task by carring it out-in research on interactive task learning in human-robot interaction (Zarrieß and Schlangen, 2018;Carlmeyer et al, 2018) ing interactive tasks (Gordon et al, 2018;Das et al, 2018) in physically simulated environments (Anderson et al, 2018;Tan and Bansal, 2018) often drawing on the successes of deep learning and reinforcement learning (Branavan et al, 2009;Liu and Chai, 2015). A lesson that can be learned from this line of research is that one main factor that affects grounding is the choice of medium of communication.…”

Section: Descriptionmentioning

confidence: 99%

Achieving Common Ground in Multi-modal Dialogue

Alikhani

Stone

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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Section: Reading Listmentioning

confidence: 99%

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

2020

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While deep learning has transformed the natural language processing (NLP) field and impacted the larger computational linguistics community, the rise of neural networks is stained by their opaque nature: It is challenging to interpret the inner workings of neural network models, and explicate their behavior. Therefore, in the last few years, an increasingly large body of work has been devoted to the analysis and interpretation of neural network models in NLP.This body of work is so far lacking a common framework and methodology. Moreover, approaching the analysis of modern neural networks can be difficult for newcomers to the field. This tutorial aims to fill this gap and introduce the nascent field of interpretability and analysis of neural networks in NLP. This tutorial will cover cutting-edge research in interpretability and analysis of modern neural NLP models. The topic has not been previously covered in *CL tutorials.

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The Hesitating Robot - Implementation and First Impressions

Cited by 7 publications

References 7 publications

EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

Achieving Common Ground in Multi-modal Dialogue

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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