{"url":"/dataset/motion-policy-networks","name":"Motion Policy Networks","full_name":null,"description_markdown":"This dataset contains a large set (~3.2 Million) of high quality expert trajectories generated from a geometrically consist hybrid planner in a wide variety of environment (~575,000 environments). We created this dataset to explore the capabilities of neural networks to learn complex robotic motion, mimicking a traditional planner. \r\n\r\nFor more information on how to use this data, please refer to the Github for this project: https://github.com/nvlabs/motion-policy-networks","description_withheld":null,"homepage":"https://zenodo.org/record/7130512","introduced_date":"2022-10-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/motion-policy-networks","title":"Motion Policy Networks","first_author":"Adam Fishman","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Actions","url":"/datasets/modality/actions"}],"tasks":[{"name":"Motion Planning","url":"/task/motion-planning","datasets_with_task":"/datasets/task/motion-planning"},{"name":"Imitation Learning","url":"/task/imitation-learning","datasets_with_task":"/datasets/task/imitation-learning"}],"languages":[],"variants":["Motion Policy Networks"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}