{"url":"/dataset/sparrow","name":"Sparrow","full_name":"Sparrow-V0: A Reinforcement Learning Friendly Simulator for Mobile Robot","description_markdown":"Sparrow-V0: A Reinforcement Learning Friendly Simulator for Mobile Robot\r\n\r\nFeatures:\r\nVectorizable (Enable fast data collection; Single environment is also supported)\r\nDomain Randomization (control interval, control delay, maximum velocity, inertia, friction, the magnitude of sensor noise and maps can be randomized while training)\r\nLightweight (Consume only 150~200 mb RAM or GPU memories per environment)\r\nStandard Gym API with both Pytorch/Numpy data flow\r\nGPU/CPU are both acceptable (If you use Pytorch to build your RL model, you can run your RL model and Sparrow both on GPU. Then you don't need to transfer the transitions from CPU to GPU anymore.)\r\nEasy to use (30kb pure Python files. Just import, never worry about installation)\r\nUbuntu/Windows are both supported\r\nAccept image as map (Customize your own environments easily and rapidly)\r\nDetailed comments on source code.","description_withheld":null,"homepage":"https://github.com/XinJingHao/Sparrow-V0","introduced_date":"2023-05-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/train-a-real-world-local-path-planner-in-one","title":"Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity","first_author":"Jinghao Xin","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[{"name":"Robot Navigation","url":"/task/robot-navigation","datasets_with_task":"/datasets/task/robot-navigation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Sparrow"],"data_loaders":[],"num_papers_in_archive":2,"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."}