Omkar2118/datasets huggingface dataset:
short-MetaWorld
Full name: MetaWorld with 20 step trajectories

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.

Introducing paper: SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
DocRED-IE

The *DocRED Information Extraction (DocRED-IE)* dataset extends the DocRED dataset for the *Document-level Closed Information Extraction (DocIE)* task. DocRED-IE is a multi-task dataset and allows for 5 subtasks: (i) Document-level Relation Extraction, (ii) Mention Detection, (iii) Entity Typing, (iv) Entity Disambiguation, (v) Coreference Resolution, as well as combinations thereof such as Named Entity Recognition (NER) or Entity Linking. The DocRED-IE dataset also allows for the end-to-end tasks of: (i) DocIE and (ii) Joint Entity and Relation Extraction. DocRED-IE comprises sentence-level and document-level facts, thereby describing short as well as long-range interactions within an entire document.

Introducing paper: REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking