File size: 5,410 Bytes
0fd7c56 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | ---
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"])
``` |