yilin-wu/droid_ood_data

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

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"])