Datasets:
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a44dd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | 1. Overview
Short-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.
2. File Structure
This directory contains 3 sub-directories.
└── short-MetaWorld
├── task_description.py # language instructions for each task
├── img_only # rendered visual inputs for all tasks
│ ├── button-press-topdown-v2 # task name
│ │ ├── 0 # trajectory id
│ │ │ ├── 0.jpg # step 0 observation (224*224)
│ │ │ ├── 1.jpg # step 1 observation
│ │ │ └── ...
│ │ ├── 1
│ │ └── ...
│ ├── door-open-v2
│ └── ...
├── unprocessed # trajectory actions with visual observations (256*256)
│ ├── unprocessed_MT10_20 # task name
│ │ ├── data.pkl # a file contains all 10 tasks
│ │ ├── door-open-v2.pkl # door-open task file
│ │ └── ...
│ └── unprocessed_ML10_20
└── r3m-processed # trajectory actions with visual obs processed by R3M [2]
3. Contact
If you have any questions, please contact liangzx@connect.hku.hk
[1] Yu, Tianhe, et al. "Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning." Conference on robot learning. PMLR, 2020.
[2] Nair, Suraj, et al. "R3M: A Universal Visual Representation for Robot Manipulation." Conference on Robot Learning. PMLR, 2023.
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