{"url":"/dataset/pushworld","name":"PushWorld","full_name":null,"description_markdown":"**PushWorld** is an environment with simplistic physics that requires manipulation planning with both movable obstacles and tools. It contains more than 200 PushWorld puzzles in PDDL and in an OpenAI Gym environment.\r\n\r\nSource: [PushWorld: A benchmark for manipulation planning with tools and movable obstacles](https://arxiv.org/pdf/2301.10289v1.pdf)","description_withheld":null,"homepage":"https://deepmind-pushworld.github.io/play/","introduced_date":"2023-01-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/pushworld-a-benchmark-for-manipulation","title":"PushWorld: A benchmark for manipulation planning with tools and movable obstacles","first_author":"Ken Kansky","url":null},"license":null,"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[{"name":"Reinforcement Learning (RL)","url":"/task/reinforcement-learning-1","datasets_with_task":"/datasets/task/reinforcement-learning-1"},{"name":"Motion Planning","url":"/task/motion-planning","datasets_with_task":"/datasets/task/motion-planning"}],"languages":[],"variants":["PushWorld"],"data_loaders":[],"num_papers_in_archive":1,"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."}