Enabling Non-Technical Domain Experts to Create Robot-Assisted Therapeutic Scenarios via Visual Programming

Schütze, Christian; Groß, André; Wrede, Britta; Richter, Birte

doi:10.1145/3536220.3558072

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…However, the defined syntax of this description language still entails difficulties ( Groß et al, 2022 ). Schütze et al (2022) focused on the design process of robot behaviors for an alpha version of RISE to reduce the effort of creating behaviors via XML. This work shows that a visual programming component contributes to improving the usability of the system significantly for experts as well as non-experts.…”

Section: Discussionmentioning

confidence: 99%

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

Groß,

Schütze,

Brandt

et al. 2023

Front. Robot. AI

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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.

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Section: Discussionmentioning

confidence: 99%

“…For a prior state of the CRAs ( Groß et al, 2022 ), a visual programming component was designed and tested ( Schütze et al, 2022 ). Here, domain experts with no knowledge about robotics were able to understand, change, and design these configurations using the visual programming component.…”

Section: Rise In Applicationmentioning

confidence: 99%

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

Groß,

Schütze,

Brandt

et al. 2023

Front. Robot. AI

Self Cite

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“…On the robot’s side, we used a modified architecture as middleware of an existing system to control different behaviors of the robot by given inputs from the scenario ( Groß et al, 2022 ). These works allow the configuration of robot-behaviors in eXtensible-Markup-Language (XML) format and the execution of such behaviors via a network also for non-computer scientists ( Schütze et al, 2022 ). The Unity Robotics Hub 6 was used to exchange information between scenario and robot via a Transmission Control Protocol (TCP)-connection via network.…”

Section: Methodsmentioning

confidence: 99%

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

Groß,

Singh,

Banh

et al. 2023

Front. Robot. AI

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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.

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“…Through a shallow grid search, we obtained optimized results for all participants with a parameter setting of 500 trees, a maximum tree depth of 10, a minimum sample split of 3, and a minimum of 4 samples per leaf. Further, a combination of all features from the delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (12)(13)(14)(15)(16)(17)(18)(19)(20), and high beta (20-30 Hz) bands binned in 2 Hz led to the optimal performance for 4-12 Hz in the first classification task (ambient vs. distraction) and for 4-20 Hz in the second classification (distraction vs. hesitation), and 62 × 5 = 310 and 62 × 9 = 558 dimensional feature vectors, respectively.…”

Section: Classificationmentioning

confidence: 99%

“…The following study would not have been conducted without LabLinking, since it builds on the complementary expertise and equipment of two laboratories: the Medical Assistance Systems Group (MAS) at Bielefeld University with its rich expertise in social robotics based on robots such as Pepper, Nao, or Flobi [19][20][21], and the Cognitive Systems Lab (CSL) at University of Bremen with vast experience in biosignal-adaptive cognitive systems [22] based on multimodal biosignal acquisition [23] and processing using machine learning methods [24], including the recording and interpretation of spoken communication [25] and high-density EEG in the context of intelligent robots and systems [26].…”

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

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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.

show abstract

Enabling Non-Technical Domain Experts to Create Robot-Assisted Therapeutic Scenarios via Visual Programming

Cited by 7 publications

References 12 publications

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

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

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

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

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