HabiCrowd, a new dataset and benchmark for crowd-aware visual navigation that surpasses other benchmarks in terms of human diversity and computational utilization. HabiCrowd can be utilized to study crowd-aware visual navigation tasks. A notable feature of HabiCrowd is that our crowd-aware settings is 3D, which is scarcely studied by previous works.
The scene derives from photo-realistic HM3D datasets. Our dataset offers a wide variety of environments especially for Social Navigation tasks, with carefully calibrated human density, incorporating realistic human motions and natural movement patterns. These features ensure balanced interaction dynamics across diverse scenes, facilitating the development of more effective social navigation algorithms.
The scene derives from photo-realistic MP3D datasets. Our dataset offers a wide variety of environments especially for Social Navigation tasks, with carefully calibrated human density, incorporating realistic human motions and natural movement patterns. These features ensure balanced interaction dynamics across diverse scenes, facilitating the development of more effective social navigation algorithms.
Introduction This dataset was gathered during the Vid2RealHRI study of humans’ perception of robots' intelligence in the context of an incidental Human-Robot encounter. The dataset contains participants' questionnaire responses to four video study conditions, namely Baseline, Verbal, Body language, and Body language + Verbal. The videos depict a scenario where a pedestrian incidentally encounters a quadruped robot trying to enter a building. The robot uses verbal commands or body language to try to ask for help from the pedestrian in different study conditions. The differences in the conditions were manipulated using the robot’s verbal and expressive movement functionalities. ## Dataset Purpose The dataset includes the responses of human subjects about the robots' social intelligence used to validate the hypothesis that robot social intelligence is positively correlated with human compliance in an incidental human-robot encounter context. The video based dataset was also developed to obtain empirical evidence that can be used to design future real-world HRI studies. ## Dataset Contents * Four videos, each corresponding to a study condition. * Four sets of Perceived Social Intelligence Scale data. Each set corresponds to one study condition * Four sets of compliance likelihood questions, each set include one Likert question and one free-form question * One set of Godspeed questionnaire data. * One set of Anthropomorphism questionnaire data. * A csv file containing the participants demographic data, Likert scale data, and text responses. * A data dictionary explaining the meaning of each of the fields in the csv file. **More details and access to this data are available from the Texas Data Repository. ** This research is part of the Vid2Real project, supported by NSF Award #2219236 and Good Systems, a Grand Research Challenge at the University of Texas at Austin.