{"url":"/dataset/short-metaworld","name":"short-MetaWorld","full_name":"MetaWorld with 20 step trajectories","description_markdown":"1. Overview\r\nShort-MetaWorld is a dataset rendered from modified environment of Meta-World [1], which contains Multi-Task10(MT10) and Meta-Learning10(ML10) in total 20 tasks with 100 successful trajectories for each task. Each trajectory is padded to 20 steps.\r\n\r\n2. File Structure\r\nThis directory contains 3 sub-directories.\r\n\r\n└── short-MetaWorld\r\n    ├── task_description.py           # language instructions for each task\r\n    ├── img_only                      # rendered visual inputs for all tasks\r\n    │   ├── button-press-topdown-v2   # task name\r\n    │   │     ├── 0                   # trajectory id     \r\n    │   │     │   ├── 0.jpg           # step 0 observation (224*224)\r\n    │   │     │\t  ├── 1.jpg           # step 1 observation\r\n    │   │     │   └── ...\r\n    │   │     ├── 1\r\n    │   │     └── ...\r\n    │   ├── door-open-v2\r\n    │   └── ...\r\n    ├── unprocessed     \t   # trajectory actions with visual observations (256*256)\r\n    │   ├── unprocessed_MT10_20    # task name\r\n    │   │   ├── data.pkl           # a file contains all 10 tasks\r\n    │   │   ├── door-open-v2.pkl   # door-open task file\r\n    │   │   └── ...\r\n    │   └── unprocessed_ML10_20\r\n    └── r3m-processed              # trajectory actions with visual obs processed by R3M [2]\r\n\r\n3. Contact\r\nIf you have any questions, please contact liangzx@connect.hku.hk\r\n\r\n[1] Yu, Tianhe, et al. \"Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.\" Conference on robot learning. PMLR, 2020.\r\n[2] Nair, Suraj, et al. \"R3M: A Universal Visual Representation for Robot Manipulation.\" Conference on Robot Learning. PMLR, 2023.","description_withheld":null,"homepage":"https://connecthkuhk-my.sharepoint.com/personal/liangzx_connect_hku_hk/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fliangzx%5Fconnect%5Fhku%5Fhk%2FDocuments%2Fshort%2DMetaWorld&ga=1","introduced_date":"2023-12-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/skilldiffuser-interpretable-hierarchical","title":"SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution","first_author":"Zhixuan Liang","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[{"name":"Trajectory Planning","url":"/task/trajectory-planning","datasets_with_task":"/datasets/task/trajectory-planning"}],"languages":[],"variants":["short-MetaWorld"],"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."}