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bencxr/
merged_pour_coke_with_videos

Name: merged_pour_coke_with_videos
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Merged Pour Coke Dataset (With Videos)

This dataset combines three pour coke datasets for disturbance recovery research:

pour_coke_static - Clean demonstrations (episodes 0-49)
pour_coke_perturbate_source - Source perturbations on black cup (episodes 50-69)
pour_coke_perturbate_target - Target perturbations on white cup (episodes 70-89)

Dataset Information

Total Episodes: 90
Total Frames: 53,644
Robot: SO-101 Follower (6-DOF arm)
FPS: 15
LeRobot Version: v3.0
Videos: Included (3 camera views, MP4 format)

Task Description

Pick up black cup and pour into white cup

Episode Breakdown

Bucket A: Clean Demonstrations (Episodes 0-49)

50 episodes of clean, unperturbed demonstrations
Static environment with no disturbances
Baseline behavior for comparison

Bucket B1: Source Perturbations (Episodes 50-69)

20 episodes with perturbations to the black cup (source object)
Grasp disturbances during approach phase
Tests recovery from source object displacement

Bucket B2: Target Perturbations (Episodes 70-89)

20 episodes with perturbations to the white cup (target object)
Target disturbances during alignment phase
Tests recovery from target object displacement

Robot Configuration

The SO-101 robot has 6 degrees of freedom:

shoulder_pan - Base rotation (yaw)
shoulder_lift - Shoulder pitch
elbow_flex - Elbow pitch
wrist_flex - Wrist pitch
wrist_roll - Wrist rotation (roll)
gripper - Gripper open/close (0 = closed, higher = open)

Camera Views

The dataset includes 3 synchronized camera views:

shoulder_base_wide_view - Wide angle view from shoulder base
workspace_variable_view - Adjustable workspace overview
wrist_roll_top_down - Top-down view from wrist

All videos are stored as MP4 files at 15 FPS, 640x480 resolution.

Data Format

Each sample contains:

python

{
    'action': [float32] * 6,                    # Target joint positions
    'observation.state': [float32] * 6,         # Current joint positions
    'timestamp': float32,                       # Time in seconds
    'frame_index': int64,                       # Frame index within episode
    'episode_index': int64,                     # Episode identifier (0-89)
    'index': int64,                             # Global frame index
    'task_index': int64                         # Task identifier (0)
}

Video Organization

Videos are organized by camera and episode chunk:

videos/
├── observation.images.shoulder_base_wide_view/
│   ├── chunk-000/  # Episodes 0-49
│   ├── chunk-050/  # Episodes 50-69
│   └── chunk-070/  # Episodes 70-89
├── observation.images.workspace_variable_view/
│   ├── chunk-000/
│   ├── chunk-050/
│   └── chunk-070/
└── observation.images.wrist_roll_top_down/
    ├── chunk-000/
    ├── chunk-050/
    └── chunk-070/

Usage

Load the dataset

python

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("bencxr/merged_pour_coke_with_videos")

# Access training split
train = dataset['train']

# Get a sample
sample = train[0]
print(f"Episode: {sample['episode_index']}")
print(f"Action: {sample['action']}")
print(f"State: {sample['observation.state']}")

Access videos

Videos are stored separately in the videos/ directory. To access them:

python

from pathlib import Path
from huggingface_hub import snapshot_download
import cv2

# Download the entire dataset including videos
dataset_path = snapshot_download(
    repo_id="bencxr/merged_pour_coke_with_videos",
    repo_type="dataset"
)

# Load a video
video_path = Path(dataset_path) / "videos/observation.images.shoulder_base_wide_view/chunk-000/file-000.mp4"
cap = cv2.VideoCapture(str(video_path))

# Read frames
ret, frame = cap.read()

Using with LeRobot

This dataset follows LeRobot v3.0 format and can be used with the LeRobot library:

python

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("bencxr/merged_pour_coke_with_videos")

Use Cases

This dataset is designed for:

Disturbance Recovery Research: Study how policies handle unexpected object displacements
Robust Policy Learning: Train policies that generalize to perturbations
Stage-Aware Reward Modeling (SARM): Learn task-stage-specific reward models
Behavior Cloning with Recovery: Train policies with both clean and disturbed demonstrations
Sim-to-Real Transfer: Evaluate robustness to real-world disturbances

Citation

If you use this dataset, please cite:

bibtex

@dataset{merged_pour_coke_2026,
  title={Merged Pour Coke Dataset with Disturbance Recovery},
  author={Dhabaria, Anjali and others},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/bencxr/merged_pour_coke_with_videos}}
}

Source Datasets

This is a merged version of:

License

Apache 2.0

Acknowledgments

Original datasets collected by Anjali Dhabaria. This merged version was created for disturbance recovery research in robotic manipulation.

Created

Dataset merged and prepared on 2026-01-31.