Create README.md

README.md

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+---
+language:
+  - en
+task_categories:
+  - robotics
+  - reinforcement-learning
+tags:
+  - robotics
+  - manipulation
+  - imitation-learning
+  - world-model
+  - robot-learning
+  - tabletop
+pretty_name: "World Model Robot Manipulation Dataset (Our-50)"
+size_categories:
+  - n<1K
+---
+
+# World Model Robot Manipulation Dataset
+
+A dataset of real-robot tabletop manipulation trajectories collected for world model training and imitation learning research. The setup follows DROID Dataset. Each trajectory pairs multi-camera video, proprioceptive state/action sequences, natural language task descriptions, and dense reward annotations with pre-extracted visual latents.
+
+## Dataset Summary
+
+| Split | Trajectories | Success Rate | Avg. Length |
+|-------|-------------|--------------|-------------|
+| Train | 250         | 44.8%        | 118 frames  |
+| Val   | 100         | 44.0%        | 106 frames  |
+| **Total** | **350** | **44.6%**  | **115 frames** |
+
+Five tabletop manipulation tasks, 50 train / 20 val trajectories per task.
+
+## Tasks
+
+| Task ID | Description | Train SR | Val SR |
+|---------|-------------|----------|--------|
+| `bag_our`    | Pick up a bag of chips and place it on a green plate | 54% | 60% |
+| `marker_our` | Pick up a marker and place it in a cup/mug           | 36% | 30% |
+| `pour_our`   | Pick up a cup of beans and place them in a bowl      | 34% | 30% |
+| `stack_our`  | Pick up a bowl and stack it on top of another bowl   | 60% | 60% |
+| `towel_our`  | Pick up a towel and place it in a basket             | 40% | 40% |
+
+Each task has multiple natural-language paraphrases (e.g. *"put the marker in the cup"*, *"put the marker in the mug"*, *"pick up the marker and place it in the cup"*).
+
+## Data Structure
+
+```
+world_model_data_our_50/
+├── annotations/
+│   ├── train/   {0..249}.json
+│   └── val/     {0..99}.json
+├── annotation_rewards/
+│   ├── train/   {0..249}.json       # same schema as annotations, includes reward fields
+│   └── val/     {0..99}.json
+├── latents/
+│   ├── train/   {0..249}_sd3.npz
+│   └── val/     {0..99}_sd3.npz
+├── videos/
+│   ├── train/   {0..249}.mp4
+│   └── val/     {0..99}.mp4
+├── norm_stats_recorded.json
+└── norm_stats_relabel.json
+```
+
+### Annotation JSON Schema
+
+Each `.json` file contains one trajectory with the following fields:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `episode_id` | int | Sequential trajectory index within the split |
+| `episode_id_orig` | str | Original episode identifier (e.g. `bag_our_003`) |
+| `texts` | list[str] | Natural language task descriptions |
+| `text_features` | float[768] | Pre-computed text embedding |
+| `success` | int | Binary success label (1 = task completed) |
+| `video_length` | int | Number of frames in the trajectory (32–334) |
+| `video_path` | str | Relative path to the `.mp4` file |
+| `latent_path` | str | Relative path to the latent `.npz` file |
+| `num_cameras` | int | Always 3 |
+| `states` | float[T][7] | Raw proprioceptive state per frame |
+| `observation.state.cartesian_position` | float[T][6] | End-effector Cartesian pose (x, y, z, rx, ry, rz) |
+| `observation.state.joint_position` | float[T][7] | 7-DOF joint positions |
+| `observation.state.gripper_position` | float[T][1] | Gripper opening |
+| `action.cartesian_position` | float[T][6] | Cartesian position action |
+| `action.joint_position` | float[T][7] | Joint position action |
+| `action.joint_velocity` | float[T][7] | Joint velocity action |
+| `action.gripper_position` | float[T][1] | Gripper action |
+| `reward_progress` | float[T] | Dense progress reward |
+| `reward_success` | float[T] | Success-shaped reward |
+| `reward_binary` | float[T] | Binary reward signal |
+
+### Video Format
+
+- Resolution: **960 × 192** (three 320 × 192 camera views (left, right, wrist) concatenated horizontally)
+- Codec: H.264
+- Frame rate: **5 fps**
+- Length: 32–334 frames per trajectory
+
+### Visual Latents
+
+Pre-extracted with **Stable Diffusion 3** (SD3). Stored as `float16` NumPy arrays.
+
+```
+latents.npz  →  key: "latents"
+shape: (3, T, 60, 256)
+        │  │   │    └─ channel dim
+        │  │   └─ spatial tokens
+        │  └─ frames
+        └─ cameras
+```
+
+### Normalization Statistics
+
+`norm_stats_recorded.json` and `norm_stats_relabel.json` provide mean/std statistics for the `state` and `actions` modalities, suitable for normalizing inputs during training.
+
+## Robot Setup
+
+- **Robot**: Franka Emika Robot arm with parallel-jaw gripper (Robotiq Gripper)
+- **Cameras**: 3 fixed cameras providing left, right, and wrist views
+- **Control frequency**: 5 Hz (matches video frame rate)
+
+## Usage Example
+
+```python
+import json
+import numpy as np
+
+# Load a trajectory
+with open("annotations/train/0.json") as f:
+    traj = json.load(f)
+
+print(traj["texts"])          # ['pick up the bag of chips and place it on the green plate']
+print(traj["success"])        # 1
+print(traj["video_length"])   # e.g. 112
+
+# Joint positions: shape (T, 7)
+joint_pos = np.array(traj["observation.state.joint_position"])
+
+# Actions: shape (T, 7)
+actions = np.array(traj["action.joint_position"])
+
+# Visual latents: shape (3, T, 60, 256)
+lat = np.load(traj["latent_path"].replace("latents/", "latents/"))["latents"]
+
+# Rewards: shape (T,)
+rewards = np.array(traj["reward_progress"])
+```