| 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. |
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| 2. File Structure |
| This directory contains 3 sub-directories. |
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| └── 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] |
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| 3. Contact |
| If you have any questions, please contact liangzx@connect.hku.hk |
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| [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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