Update README.md

README.md

@@ -5,143 +5,73 @@ task_categories:
 - reinforcement-learning
 tags:
 - metaworld
+- short-metaworld
 - robotics
 - manipulation
 - multi-task
-- r3m
 - vision-language
-- imitation
+- imitation-learning
+- r3m
 size_categories:
-- 1K<n<10K
+- 10K<n<100K
 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
+pretty_name: Short-MetaWorld-VLA (v2+v3)
 ---
 
-# Short-MetaWorld Dataset
+# Short-MetaWorld-VLA (v2 + v3)
 
 ## 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
+This dataset contains a short MetaWorld collection used for VLA-style training and evaluation.
 
-# Load the dataset
-dataset = load_short_metaworld("./", image_size=224)
+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
 
-# Create a DataLoader
-dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+## Dataset Structure
 
-# 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-vla/
+├── mt50_task_prompts.json
+├── short_metaworld_loader.py
+├── requirements.txt
 ├── 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
-```
+│ ├── img_only/
+│ │ └── <task>/<trajectory>/<step>.jpg
+│ └── r3m-processed/
+�� └── r3m_MT10_20/
+│ ├── <task>-v2.pkl
+│ ├── <task>-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
 
@@ -153,49 +83,6 @@ This dataset is designed for:
 - **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: liangzx@connect.hku.hk
-- Questions about this upload: Open an issue in the dataset repository
 
 ## ⚖️ License