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25 results for “Human-robot Interaction”
Postural Optimization for a Safe and Comfortable Human-Robot Interaction: Experiment Dataset
<p>In human-robot collaboration the robot's behavior impacts the worker's safety, comfort, and his acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human kinematic model, we optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures. We then derive a robotic behavior that leads the worker towards that improved posture. We validate our approach in an experiment involving a joint task with 39 human subjects and a Baxter torso-humanoid robot.</p> <p>This repository contains the anonymized recorded data of our experiment. For all the subjects, we have included their recorded posture, using a motion capture system, and a video taken from a camera located on the robot head. Each data is divided by subjects and by tested conditions. In a separate file, we also include the result of the survey answered alongside the experiment.</p>
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
IntelliMan_WP4_Adaptive Shared Autonomy_T4.2_Advanced human-robot interaction modalities_human robot handover_v0
<p><span>The dataset contains data related to the experiments presented in the publication:</span></p> <p><em><span>M. Costanzo, C. Natale and M. Selvaggio, "Visual and Haptic Cues for Human-Robot Handover*," 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2677-2682, doi: 10.1109/RO-MAN57019.2023.10309480.</span></em></p>
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) Dataset - Anonymized
<p>Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.</p> <p>The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.</p> <p><strong>Exchanges</strong> file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange. </p> <p><strong>Overall </strong>file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction.</p> <p>The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns. </p> <p><em>Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.</em></p> <p>Anonymization macros:</p> <p>[P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given.</p> <p>[NAME_REMOVED]: A name of another person mentioned by the participant.</p> <p>[LOCATION_REMOVED]: Small town/village/area where the participant lives or lived.</p> <p>[MEDICAL_INFO_REMOVED]: Medical information shared by the participant.</p> <p>[AGE_REMOVED]: Participant's or other person's age.</p> <p>[INFORMATION_REMOVED]: Sensitive information shared by the participant.</p> <p>[MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.</p>
Quantitative Survey: Human-Robot Interaction
<p>This dataset includes survey data from 306 respondents who were international postgraduate students from five European universities; four in the UK and one in Spain. They were each randomly exposed to a picture of a robot--either Geminoid DK, AIBO, or Pepper.</p>
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