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52 results for “Social Robotics”
Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework
<p>This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play. </p> <p>In this data set, <strong>team level multi-modal behavioral data</strong> is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below: </p> <ul> <li><em>condition:</em> This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li><em>Error: </em>This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li><em>Learning Gain: </em>It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li><em>Usefulness Score: </em>The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li><em>PE Score: </em>It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li><em>Right_Suggestions: </em>This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li><em>Right_Time: </em>This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li><em>Exploration:</em> This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li><em>Reflection: </em>This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li><em>Communication: </em>This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li><em>LG_status: </em>This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> <p>This dataset corresponds to the publication <em><strong>"Social robots as skilled ignorant peers for supporting learning"</strong></em>: <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a></p> <p> </p>
User Study Data for Paper "A case study in designing trustworthy interactions: implications for socially assistive robotics"
<p>Experimental data collected for the user study described in Frontiers paper "A case study in designing trustworthy interactions: implications for socially assistive robotics" by Mengyu Zhong et al. Citation: <i>Zhong, Mengyu, et al. "A case study in designing trustworthy interactions: implications for socially assistive robotics." Frontiers in Computer Science 5.1152532 (2023). </i></p>
Brain activity during reciprocal social interaction investigated using conversational robots as control condition
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Dataset of "Social Robots and Sensors for Enhanced Ageing at Home: A Focus on Mobility and Socioeconomic Factors."
<p>This dataset supports the article:</p> <p>"Social Robots and Sensors for Enhanced Aging at Home: A Focus on Mobility and Socioeconomic Factors."</p> <p>For further details see the Readme.txt file.</p>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Benchmark EEG data set for trust assessment for interactions with social robots
<p>The data collection consisted of a game interaction with a small humanoid EZ-robot. The robot explains a word to the participant either through movements depicting the concept or by verbal description. Depending on their performance, participants could "earn" or loose candy as remuneration for their participation.</p> <p>The dataset comprises EEG (Electroencephalography) recordings from 21 participants, gathered using Emotiv headsets. Each participant's EEG data includes timestamps and measurements from 14 sensors placed across different regions of the scalp. The sensor labels in the header are as follows: EEG.AF3, EEG.F7, EEG.F3, EEG.FC5, EEG.T7, EEG.P7, EEG.O1, EEG.O2, EEG.P8, EEG.T8, EEG.FC6, EEG.F4, EEG.F8, EEG.AF4, and Time.</p> <p>The EEG data provides insights into the electrical activity of the brain, offering a window into cognitive processes and emotional responses during various activities or stimuli in the form of microvolt and with a frame rate of 128 Hz. The whole data set consists of 3651124 data points for each sensor, i.e. 173863 on average for each participant (min. 128505, max. 249631). </p> <p>Files are named after participant numbers starting with ID01. The data has to be pre-processed making use of the information given in the details.xlsx file that contains annotations corresponding to the EEG recordings. These annotations denote the timing of different phases related to trust across the participants' interactions. Each phase is delineated by a start time and an end time, representing distinct stages of the trust-building process. All the other data (timestamps) which are outside the start and end of each phase should be considered as breaks, e.g. filling out the questionnaires. The last element is the trust score for the given phase, which is calculated on the answers in an MDMT questionnaire.</p> <p>The following phases have been annotated:</p> <ol> <li>Trust Building: This phase involves friendly initial interactions for establishing trust between participants and the robot.</li> <li>Situational Awareness: This phase continues to build up trust by showing situation awareness of the robot, e.g. by complimenting on the participant's fashion choice.</li> <li>Transparency: Trust is maintained by increased openness and clarity in communicating about the robot's abilities.</li> <li>Trust Violation: Trust is compromised during this phase by deliberately misleading the participant and making it impossible to answer correctly. </li> <li>Trust Repair: The robot shows efforts to repair trust by apologizing for the behavior in the previous stage.</li> </ol> <p>If you work with the data, please cite one of the article given below.</p>
Data from: Social robots as conversational catalysts: Enhancing long-term human-human dyadic interaction at home
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Data for the publication "Socially competent robots"
<p>Data for the publication "Socially competent robots".</p>
CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification
<p>Video of submitted paper entitled "CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification"</p>
Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise
<p>Video of submitted paper entitled "Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise"</p>
Effect Of Social Robots In Cerebral Palsy
ClinicalTrials.gov study NCT06948227. IPD Sharing: NO. Countries: 1. Publications: 14.
Robotic Intervention Framework for Children With Autism Spectrum Disorder to Enhance Social Engagement and Participation
ClinicalTrials.gov study NCT04879303. IPD Sharing: NO. Countries: 1. Publications: 1.
Study of the Impact of a Social Telepresence Robot on Social Isolation and Loneliness of the Elderly at Home
ClinicalTrials.gov study NCT04767100. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of a Proactive Social Robot for Older Adults in Reducing Loneliness and Social Isolation
ClinicalTrials.gov study NCT03972787. IPD Sharing: NO. Countries: 2. Publications: 17.
Social Robotics for Autism
ClinicalTrials.gov study NCT06974240. IPD Sharing: NO. Countries: 1. Publications: 2.
" Socially Pertinent Robot in Gerontological Healthcare "
ClinicalTrials.gov study NCT05089799. IPD Sharing: YES. Countries: 1. Publications: 1.
AI-generated Feedback in Social Robotic Virtual Patients
ClinicalTrials.gov study NCT07277829. IPD Sharing: YES. Countries: 1. Publications: 2.
Testing the Efficacy of Social Robot-led Yoga
ClinicalTrials.gov study NCT05476614. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Psycho-social Intervention With Paro Robotic Seal Focused on Patients With Dementia
ClinicalTrials.gov study NCT05626205. IPD Sharing: Not stated. Countries: 1. Publications: 1.
engAGE: Managing cognitivE decliNe throuGh Theatre Therapy, Artificial Intelligence and Social Robots drivEn Interventions
ClinicalTrials.gov study NCT06302686. IPD Sharing: Not stated. Countries: 3. Publications: 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.