Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu, Eduardo Gutierrez Maestro, Lucas Morillo-Mendez, Andrey Rudenko, Luigi Palmieri, Tomasz P Kucner, Martin Magnusson and Achim J Lilienthal
The International Journal of Robotics Research
Abstract
We present a new large dataset of indoor human and robot navigation and interaction, called THOR-MAGNI, that is ¨
designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THOR-MAGNI was created to ¨ fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THOR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THOR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions
@article{doi:10.1177/02783649241274794,
author = {Tim Schreiter and Tiago Rodrigues de Almeida and Yufei Zhu and Eduardo Gutierrez Maestro and Lucas Morillo-Mendez and Andrey Rudenko and Luigi Palmieri and Tomasz P Kucner and Martin Magnusson and Achim J Lilienthal},
title ={THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction},
journal = {The International Journal of Robotics Research},
volume = {0},
number = {0},
pages = {02783649241274794},
year = {0},
doi = {10.1177/02783649241274794},
URL = {https://doi.org/10.1177/02783649241274794},
eprint = {https://doi.org/10.1177/02783649241274794
}