{"url":"/dataset/jrdb","name":"JRDB","full_name":"JackRabbot Dataset and Benchmark","description_markdown":"A novel egocentric dataset collected from social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 degrees RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360 degrees spherical image from a fisheye camera and encoder values from the robot's wheels.\r\n\r\nSource: [JRDB: A Dataset and Benchmark of Egocentric Visual Perception for Navigation in Human Environments](/paper/jrdb-a-dataset-and-benchmark-for-visual)","description_withheld":null,"homepage":"https://jrdb.stanford.edu/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/jrdb-a-dataset-and-benchmark-for-visual","title":"JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments","first_author":"Roberto Martín-Martín","url":null},"license":null,"modalities":[],"tasks":[{"name":"Multi-Object Tracking","url":"/task/multi-object-tracking","datasets_with_task":"/datasets/task/multi-object-tracking"},{"name":"Autonomous Navigation","url":"/task/autonomous-navigation","datasets_with_task":"/datasets/task/autonomous-navigation"},{"name":"Human Detection","url":"/task/human-detection","datasets_with_task":"/datasets/task/human-detection"}],"languages":[],"variants":["JRDB"],"data_loaders":[],"num_papers_in_archive":33,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-object-tracking-on-jrdb","task":"Multi-Object Tracking","dataset_variant":"JRDB","rows":10,"metrics":["HOTA"],"first_row_in_archive_order":{"model":"OmniTrack","paper":"/paper/omnidirectional-multi-object-tracking","metrics":{"HOTA":"26.92"},"code_links":[{"title":"xifen523/omnitrack","url":"https://github.com/xifen523/omnitrack"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/omnidirectional-multi-object-tracking","title":"Omnidirectional Multi-Object Tracking","date":"2025-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}