README.md

metadata

license: mit
task_categories:
  - robotics
  - reinforcement-learning
tags:
  - metaworld
  - short-metaworld
  - robotics
  - manipulation
  - multi-task
  - vision-language
  - imitation-learning
  - r3m
size_categories:
  - 10K<n<100K
language:
  - en
pretty_name: Short-MetaWorld-VLA (v2+v3)

Short-MetaWorld-VLA (v2 + v3)

Overview

This dataset contains a short MetaWorld collection used for VLA-style training and evaluation.

Current local structure includes:

• 24 task files in r3m_MT10_20 (12 v2 + 12 v3)
• 100 trajectories per task
• 20 or 50 steps per trajectory (task/version dependent)
• 84,000 total step samples from PKL action/state streams

Dataset Structure

short-metaworld-vla/ ├── mt50_task_prompts.json ├── short_metaworld_loader.py ├── requirements.txt ├── short-MetaWorld/ │ ├── img_only/ │ │ └── //.jpg │ └── r3m-processed/ │ └── r3m_MT10_20/ │ ├── -v2.pkl │ ├── -v3.pkl │ └── data.pkl └── r3m-processed/ └── r3m_MT10_20/

Data Format

Per step:

• image: RGB frame (.jpg)
• state: 39D float vector
• action: 4D float vector
• prompt: task language instruction (from mt50_task_prompts.json)
• task_name: task identifier (e.g. button-press-topdown-v3)

Tasks

Includes both -v2 and -v3 variants such as:

• basketball
• button-press-topdown
• door-open
• drawer-open / drawer-close
• peg-insert-side
• pick-place
• push
• reach
• sweep
• window-open / window-close
• plus v3-only tasks in this dump (e.g. handle-pull-v3, stick-pull-v3)

🔬 Research Applications

This dataset is designed for:

• Multi-task Reinforcement Learning: Train policies across multiple manipulation tasks
• Imitation Learning: Learn from demonstration trajectories
• Vision-Language Robotics: Connect visual observations with natural language instructions
• Meta-Learning: Adapt quickly to new manipulation tasks
• Robot Policy Training: End-to-end visuomotor control

⚖️ License

MIT License - See LICENSE file for details.