Add comprehensive dataset card

README.md

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+---
+license: mit
+task_categories:
+- robotics
+- reinforcement-learning
+tags:
+- metaworld
+- robotics
+- manipulation
+- multi-task
+- r3m
+- vision-language
+- imitation
+size_categories:
+- 1K<n<10K
+language:
+- en
+pretty_name: Short-MetaWorld Dataset
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: state
+    dtype: 
+      sequence: float32
+  - name: action
+    dtype:
+      sequence: float32
+  - name: prompt
+    dtype: string
+  - name: task_name
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1900000000
+    num_examples: 40000
+  download_size: 1900000000
+  dataset_size: 1900000000
+---
+
+# Short-MetaWorld Dataset
+
+## Overview
+
+Short-MetaWorld is a curated dataset from Meta-World containing **Multi-Task 10 (MT10)** and **Meta-Learning 10 (ML10)** tasks with **100 successful trajectories per task** and **20 steps per trajectory**. This dataset is specifically designed for multi-task robot learning, imitation learning, and vision-language robotics research.
+
+## 🚀 Quick Start
+
+```python
+from short_metaworld_loader import load_short_metaworld
+from torch.utils.data import DataLoader
+
+# Load the dataset
+dataset = load_short_metaworld("./", image_size=224)
+
+# Create a DataLoader
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+
+# Get a sample
+sample = dataset[0]
+print(f"Image shape: {sample['image'].shape}")
+print(f"State: {sample['state']}")
+print(f"Action: {sample['action']}")
+print(f"Task: {sample['task_name']}")
+print(f"Prompt: {sample['prompt']}")
+```
+
+## 📁 Dataset Structure
+
+```
+short-MetaWorld/
+├── README.txt                     # Original dataset documentation
+├── short-MetaWorld/
+│   ├── img_only/                    # 224x224 RGB images
+│   │   ├── button-press-topdown-v2/
+│   │   │   ├── 0/                   # Trajectory 0
+│   │   │   │   ├── 0.jpg           # Step 0
+│   │   │   │   ├── 1.jpg           # Step 1
+│   │   │   │   └── ...
+│   │   │   ├── 1/                   # Trajectory 1
+│   │   │   └── ...
+│   │   ├── door-open-v2/
+│   │   └── ...
+│   └── r3m-processed/              # R3M processed features
+│       └── r3m_MT10_20/
+│           ├── button-press-topdown-v2.pkl
+│           ├── door-open-v2.pkl
+│           └── ...
+└── r3m-processed/                  # Additional R3M data
+    └── r3m_MT10_20/
+├── mt50_task_prompts.json          # Task descriptions & prompts
+├── short_metaworld_loader.py       # Dataset loader
+└── requirements.txt
+```
+
+## 🎯 Tasks Included
+
+### Multi-Task 10 (MT10)
+- `button-press-topdown-v2` - Press button from above
+- `door-open-v2` - Open door by pulling handle  
+- `drawer-close-v2` - Close drawer
+- `drawer-open-v2` - Open drawer
+- `peg-insert-side-v2` - Insert peg into hole
+- `pick-place-v2` - Pick up object and place on target
+
+### Meta-Learning 10 (ML10)
+Additional tasks for meta-learning evaluation.
+
+## 📊 Data Format
+
+- **Images**: 224×224 RGB images in JPEG format
+- **States**: 7-dimensional robot state vectors (joint positions)
+- **Actions**: 4-dimensional continuous control actions
+- **Prompts**: Natural language task descriptions in 3 styles:
+  - `simple`: Brief task description
+  - `detailed`: Comprehensive task explanation  
+  - `task_specific`: Context-specific variations
+- **R3M Features**: Pre-processed visual representations using R3M model
+
+## 💾 Loading the Dataset
+
+The dataset comes with a comprehensive loader (`short_metaworld_loader.py`):
+
+```python
+# Load specific tasks
+mt10_tasks = [
+    "reach-v2", "push-v2", "pick-place-v2", "door-open-v2", 
+    "drawer-open-v2", "drawer-close-v2", "button-press-topdown-v2",
+    "button-press-v2", "button-press-wall-v2", "button-press-topdown-wall-v2"
+]
+dataset = load_short_metaworld("./", tasks=mt10_tasks)
+
+# Load all available tasks
+dataset = load_short_metaworld("./")
+
+# Get dataset statistics
+stats = dataset.get_dataset_stats()
+print(f"Total steps: {stats['total_steps']}")
+print(f"Tasks: {stats['tasks']}")
+
+# Get task-specific prompts
+task_info = dataset.get_task_info("pick-place-v2")
+print(task_info['detailed'])  # Detailed task description
+```
+
+## 🔬 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
+
+## 📈 Dataset Statistics
+
+- **Total trajectories**: 2,000 (100 per task × 20 tasks)
+- **Total steps**: ~40,000 (20 steps per trajectory)
+- **Image resolution**: 224×224 RGB
+- **State dimension**: 7 (robot joint positions)
+- **Action dimension**: 4 (continuous control)
+- **Dataset size**: ~1.9GB
+
+## 🛠️ Installation
+
+```bash
+pip install torch torchvision Pillow numpy
+```
+
+## 📖 Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{yu2020meta,
+  title={Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning},
+  author={Yu, Tianhe and Quillen, Deirdre and He, Zhanpeng and Julian, Ryan and Hausman, Karol and Finn, Chelsea and Levine, Sergey},
+  booktitle={Conference on robot learning},
+  pages={1094--1100},
+  year={2020},
+  organization={PMLR}
+}
+
+@inproceedings{nair2022r3m,
+  title={R3M: A Universal Visual Representation for Robot Manipulation},
+  author={Nair, Suraj and Rajeswaran, Aravind and Kumar, Vikash and Finn, Chelsea and Gupta, Abhinav},
+  booktitle={Conference on Robot Learning},
+  pages={892--902},
+  year={2023},
+  organization={PMLR}
+}
+```
+
+## 📧 Contact
+
+- Original dataset: [email protected]
+- Questions about this upload: Open an issue in the dataset repository
+
+## ⚖️ License
+
+MIT License - See LICENSE file for details.