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 ---
task_categories:
- robotics
tags:
- Zeroshot
configs:
- config_name: default
data_files: data/*/*.parquet
license: apache-2.0
---
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/light_logo.svg" width="50%" alt="Zeroshot Data" />
</div>
<h3 align="center">
<span style="color: #594EE6;">Real-world data for general robotics.</span>
</h3>
<div align="center">
<a href="https://www.zeroshotdata.com/" target="_blank">
<img alt="Homepage" src="https://storage.googleapis.com/zeroshot-public-rrds/assets/zeroshot_badge.svg" />
</a>
<a href="https://x.com/ZeroShotData" target="_blank">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Zeroshot_Data-white?logo=x&logoColor=white" />
</a>
<a href="https://huggingface.co/datasets/zeroshotdata/zeroshot-test-dataset" target="_blank">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Zeroshot%20Data-ffc107?color=ffc107&logoColor=white" />
</a>
</div>
<h2>
<p style="color: #594EE6;" align="center">
<span style="color: #594EE6;">SF Fold</span>
</p>
<br/>
<br/>
# 🤖 What is Zeroshot Tshirt?
<p>This dataset contains real-world residential t-shirt folding demonstrations, collected by trained data collectors using hand-held grippers in diverse home environments.</p>
## 📖 Table of Contents
1. [Features](#📊-features)
2. [Terminology](#📖-terminology)
3. [Specifications](#🛠️-specifications)
- [Dataset Composition](#dataset-composition)
- [Environment Composition](#environment-composition)
- [Garment Composition](#garment-composition)
- [Trajectory Specifications](#trajectory-specifications)
- [Hardware Specifications](#hardware-specifications)
- [Physical Specifications](#physical-specifications)
4. [Data Format](#🗂️-data-format)
- [Conventions](#conventions)
- [Directory Layout](#directory-layout)
- [Video Files](#video-files)
- [Parquet Format](#parquet-format)
- [Frame of Reference](#frame-of-reference)
5. [Collection](#🛒-collection)
- [Collection Info](#collection-info)
- [Privacy & Consent](#privacy--consent)
- [T-Shirt Folding Procedure Guidelines](#t-shirt-folding-procedure-guidelines)
- [T-Shirt Folding Instructions](#t-shirt-folding-instructions)
6. [Preprocessing & Annotation](#✒️-preprocessing--annotation)
7. [Validation](#🔬-validation)
8. [Distribution](#🚚-distribution)
9. [Appendix](#🔗-appendix)
## 📊 Features
This dataset contains **real-world residential t-shirt folding demonstrations**, collected using hand-held grippers in diverse household environments with varied lighting differences.
| Category | Description |
| ----------------------- | ------------------------------------------------------------- |
| **Environments** | 212 unique environments across 31 locations |
| **Episodes** | 4,832 (≈ 101.4 hours) |
| **Video Steams** | 1296×972 @ 30 fps |
| **Trajectory Accuracy** | Abs. Pose Error: 10 ± 5.1 mm; Abs. Rotation Error: 1.5 ± 0.6° |
| **Data Formats** | Parquet (poses, gripper widths), MP4 (video streams) |
| **Collection Method** | In-house residential data collection using handheld grippers |
| **Validation** | Cross-validated against **OptiTrack Trio 3** |
| **Availability** | Raw data and preprocessing documentation included |
## 📖 Terminology
| Term | Definition |
| -------------- | ------------------------------------------------------------------ |
| **Puppet** | End-effector system with articulated motion axes and cameras |
| **Gripper** | Parallel-jaw mechanism with continuous width measurement |
| **Ego Camera** | Chest-mounted first-person camera for contextual observation |
| **Pose** | 6-DoF rigid body representation: (tx, ty, tz, qx, qy, qz, qw) |
| **Episode** | Atomic demonstration sequence with synchronized multimodal streams |
## 🛠️ Specifications
### Dataset Composition
| Metric | Value |
| -------------- | ----------- |
| Locations | 31 |
| Environments | 212 |
| Episodes | 4,832 |
| Total Duration | 101.4 hours |
### Environment Composition
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/environment_stats.png" width="70%" alt="Environment Composition" />
</div>
### Garment Composition
Garments include short-sleeve T-shirts of varying materials (cotton, polyester, scrubs) and collars (crew, polo).
| Garment Type | Count |
| ------------ | ----- |
| T-shirts | ≈ 200 |
| Blouses | ≈ 5 |
### Trajectory Specifications
| Metric | Value | Units |
| ----------------------------- | --------- | ------- |
| Absolute Pose Error (APE) | 10 ± 5.1 | mm |
| Relative Pose Error (RPE) | 3 ± 0.5 | mm |
| Absolute Rotation Error (ARE) | 1.5 ± 0.6 | degrees |
| Relative Rotation Error (RRE) | 1.8 ± 0.8 | degrees |
| Sampling Frequency | 30 ± 0.09 | Hz |
### Hardware Specifications
#### Camera
| Property | Value |
| ------------- | ------------ |
| Resolution | 1296×972 px |
| Frame Rate | 30 fps |
| Bitrate | 16 Mbps |
| Sensor Size | 0.25 inch |
| Field of View | 210° |
| Focal Length | 2.1 ± 0.2 mm |
#### Gripper Encoder
| Property | Value |
| ------------- | ----------- |
| Resolution | 0.000077 mm |
| Accuracy | ±0.01 mm |
| Repeatability | ±0.002 mm |
| Max Width | 85 ± 5 mm |
### Physical Specifications
#### Frame [Reference](https://zeroshotdata.github.io/docs/transforms.html)
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/3d_puppet.jpeg" width="70%" alt="3D Puppet Coordinate Frames Visualization" />
</div>
#### Frame [Values](https://zeroshotdata.github.io/docs/transform_written.html)
- T_normal_fingertip (origin to end actuator fingertip)
```plaintext
[[ 1. 0. 0. 0.01635 ]
[ 0. 1. 0. -0.016538]
[ 0. 0. 1. 0.134907]
[ 0. 0. 0. 1. ]]
```
- T_puppet_camera (origin to camera)
```plaintext
[[1. 0. 0. 0.0163 ]
[0. 1. 0. 0.035513]
[0. 0. 1. 0.02112 ]
[0. 0. 0. 1. ]]
```
## 🗂️ Data Format
Dataset follows the **[LeRobot Standard](https://docs.phospho.ai/learn/lerobot-dataset)** for robotic learning.
### Conventions
- **Episodes:** atomic trajectories (`episode_index`)
- **Frames:** time-ordered (`frame_index`)
- **Timestamps:** monotonic
- **Observations:** camera-aligned signals (images, proprioception).
- **Actions:** commanded end-effector pose + gripper width.
- **State:** measured end-effector pose + gripper width.
- **Coordinate frames:** world (static), camera (optical center), motion_axis, fingertip.
- **Units:** meters, seconds, radians
- **Rotations:** quaternions (qx, qy, qz, qw) with right-handed, w last.
### Directory Layout
```plaintext
zeroshot-tshirt/
├── meta/
│ ├── dataset.json # global metadata (versioning, sensors, units)
│ ├── tasks.jsonl # per-task descriptors (id, name, split)
│ ├── episodes.jsonl # per-episode descriptors (id, name, split)
│ └── episodes_stats.jsonl # per-episode statistics (duration, frames)
├── data/
│ └── chunk-{000..}/episode_{000000..}.parquet
├── videos/
│ └── chunk-{000..}/
│ └── observation.images.cam_ego/episode_{000000..}.mp4
│ └── observation.images.cam_left/episode_{000000..}.mp4
│ └── observation.images.cam_right/episode_{000000..}.mp4
└── calibration/
├── intrinsics_cam_ego.json
├── intrinsics_cam_left.json
├── intrinsics_cam_right.json
└── extrinsics_world.json
```
### Video Files
Three synchronized camera streams per episode:
- `cam_ego`: First-person view
- `cam_left`: Left puppet view
- `cam_right`: Right puppet view
Accessed as **VideoFrame objects** via Hugging Face interfaces.
### Parquet Format
Each row includes:
| Field | Type | Units | Shape / Length | Description |
| ----------------------- | ------------- | ------- | -------------- | ----------------------------------------------------------------------------------------------------- |
| `index` | int64 | s | scalar | Global row index (unique across dataset chunk) |
| `frame_index` | int64 | m | scalar | Frame number within the episode |
| `timestamp` | float32 | s | scalar | Time since episode start |
| `episode_index` | int64 | m, quat | scalar | Unique episode identifier |
| `gripper_width` | list[float32] | m, quat | length 2 | Parallel jaw width for left and right puppets |
| `task_index` | int64 | m, quat | scalar | Task identifier (links to tasks.jsonl) |
| `left_camera_pose` | list[float32] | m, quat | length 7 | 3D position (tx, ty, tz) + quaternion (qx, qy, qz, qw) for left camera optical center, in world frame |
| `right_camera_pose` | list[float32] | m, quat | length 7 | 3D position (tx, ty, tz) + quaternion (qx, qy, qz, qw) for left camera optical center, in world frame |
| `right_fingertip_pose` | list[float32] | m, quat | length 7 | 3D pose of midpoint between left puppet’s fingertips |
| `left_fingertip_pose` | list[float32] | m, quat | length 7 | 3D pose of midpoint between right puppet’s fingertips |
#### Example Parquet Data
```plaintext
"index": 124578,
"frame_index": 318,
"timestamp": 12.634,
"episode_index": 42,
"gripper_width": [0.034, 0.034],
"task_index": 7,
"left_camera_pose": [0.152, -0.031, 0.884, 0.002, 0.713, -0.001, 0.701],
"right_camera_pose": [0.148, 0.029, 0.882, -0.003, 0.710, 0.006, 0.704],
"left_fingertip_pose": [0.612, -0.084, 0.502, 0.002, 0.005, 0.721, 0.693],
"right_fingertip_pose": [0.616, 0.089, 0.503, -0.003, 0.004, 0.718, 0.696]
```
### Frame of Reference
All trajectories and poses are expressed in a **static world frame**. The world frame origin is defined at the **base of the right puppet**, serving as the global reference for all coordinate transforms. Camera poses, gripper positions, and fingertip poses are aligned to this frame, with translations given in meters and orientations expressed as quaternions (qx, qy, qz, qw).
## 🛒 Collection
This dataset was primarily collected in **San Francisco, California, USA**. Data collection took place in **noisy and diverse real-world environments** to capture a broad range of variability. All sessions were performed by **trained, paid data collectors** following **standardized procedures** to ensure consistency across sessions.
To enhance robustness, **failure cases**—such as tangled fabrics or irregular interactions—were **intentionally retained**, increasing the diversity of captured scenarios. Each recording was **reviewed for quality**, and only those meeting data standards were included in the final dataset.
### Collection Info
- **Location:** San Francisco, California, USA 🇺🇸
- **Collectors:** Trained, paid data collectors
- **Diversity:** Includes both successful and failure cases
### Privacy & Consent
All data collection was conducted under protocols designed to ensure privacy and **informed consent**. Participants were explicitly notified of the scope of collection, including video, image, IMU, and encoder streams, and provided documented consent prior to recording. Permission to capture data was obtained for all environments.
### T-Shirt Folding Procedure Guidelines
- **Fixed stance:** Torso facing workspace
- **Hardware:** Only in-house system used
- **Variability:** Natural irregularities retained (no retries)
The t-shirt folding task was performed under **standardized collection protocols** to ensure reproducibility and consistency across sessions. Data collectors maintained a **fixed stance** throughout demonstrations, with feet planted and torso oriented toward the workspace, avoiding lateral rotation or excessive forward lean.
All manipulations were conducted exclusively using the **in-house hardware system**. When irregularities occurred—such as tangled fabric or misaligned folds—the procedure continued rather than being restarted, preserving the **natural variability** of the task.
Across all episodes, data collectors executed the folding sequence with the goal of **efficient task completion**, minimizing extraneous motion while maintaining **data fidelity**.
### T-Shirt Folding Instructions
1. **Retrieve garment**
a. Pick up a shirt from the laundry basket.
2. **Position shirt**
a. Lay the shirt flat on a surface, front side down, with the collar aligned at the top.
3. **Fold sides**
a. Fold one side inward to the shirt’s centerline.
b. Fold the remaining side inward so both edges overlap neatly.
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/fold_0.jpeg" width="70%" alt="Folding Procedure Image 0" />
</div>
4. **Create folds**
a. Fold the shirt upward from the bottom to the midline.
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/fold_1.jpeg" width="70%" alt="Folding Procedure Image 1" />
</div>
b. Fold again from the midline to the collar to form a compact rectangle.
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/fold_2.jpeg" width="70%" alt="Folding Procedure Image 2" />
</div>
5. **Stack**
a. Place the folded shirt neatly onto the prepared stack or storage area.
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/fold_3.jpeg" width="70%" alt="Folding Procedure Image 3" />
</div>
## ✒️ Preprocessing & Annotation
Preprocessing was performed using a **lightweight filtering pipeline** designed to stabilize signals while preserving fine motion. A **Fixed-Interval Kalman filter** with a **Rauch–Tung–Striebel (RTS) smoother** was applied to refine trajectory estimates.
Rotational data and **gripper width** signals were further processed using a **double Butterworth filter** to reduce noise while maintaining sharp transitions. Filtering was implemented with **standard numerical libraries** within an **in-house software framework**.
The filtering strategy was kept **conservative**, ensuring that subtle manipulations and fine-scale movements remained intact. All **raw, unfiltered recordings** were preserved and remain available upon request.
### Preprocessing
Applied lightweight filtering with:
| Filter / Method | Applied To |
|------------------|-------------|
| Kalman + RTS Smoother | Trajectories |
| Double Butterworth | Rotational data, gripper width |
| Light Filtering | All signals |
| Raw Data Retention | All modalities |
### Labeling
- Room-type labels (bedroom, kitchen, etc.)
## 🔬 Validation
Validation was performed by recording a range of manipulation motions using the **in-house hardware system**, while simultaneously capturing ground truth with an [**OptiTrack Trio 32**](https://optitrack.com/cameras/trio-3/) motion capture system.
Signals were **time-aligned**, and **frame-wise errors** were computed for both **position** and **orientation** measurements.
### 3D Position Accuracy
Free-space trajectories were executed, including **sweeping** and **randomized motions** of the end-effector. Errors were computed **frame-by-frame** against OptiTrack ground truth data.
The **mean 3D positional error** was consistently within the **low-centimeter range**, with additional improvements observed after applying the **Fixed-Interval Kalman filter** with **RTS smoothing**.
### Orientation Accuracy
Orientation estimates were validated against **quaternion data** from the OptiTrack system.
A **double Butterworth filter** was applied to the raw rotational signals.
Across all validation trials, the system achieved **sub-degree median orientation errors**.
| Metric | Accuracy |
| ----------- | ------------- |
| 3D Position | ≤ 7 mm error |
| Orientation | ≤ 1° error |
## 🚚 Distribution
**ZeroShot** is responsible for maintaining this dataset. For any questions or concerns, please contact **[interest@zeroshotdata.com](mailto:interest@zeroshotdata.com)**.
The dataset is released as an **open-source resource** under the **Apache 2.0** license and made publicly available on Hugging Face.
**Versioning** will allow users to reference specific releases. All updates will be **documented** in this file and reflected across relevant distribution points.
Older versions may be **archived for reproducibility**, and any obsolescence will be **clearly communicated**.
The dataset contains **no third-party rights** or **export-control restrictions**.
Access will be **free of charge**, and users are encouraged to provide **proper attribution** by citing **ZeroShot** and the associated **technical documentation** when referencing this dataset in publications.
## 🔗 Appendix
### Data Samples
<div align="center">
<img src="https://storage.googleapis.com/zeroshot-public-rrds/assets/rerun_preview.jpeg" width="100%" alt="Dataset Sample Image" />
</div>
### Interactive Samples
| Sample | Link |
| ------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | [Rerun Viewer #1](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/1.rrd&renderer=webgl) |
| 2 | [Rerun Viewer #2](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/2.rrd&renderer=webgl) |
| 3 | [Rerun Viewer #3](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/3.rrd&renderer=webgl) |
| 4 | [Rerun Viewer #4](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/4.rrd&renderer=webgl) |
| 5 | [Rerun Viewer #5](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/5.rrd&renderer=webgl) |
| 6 | [Rerun Viewer #6](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/6.rrd&renderer=webgl) |
| 7 | [Rerun Viewer #7](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/7.rrd&renderer=webgl) |
| 8 | [Rerun Viewer #8](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/8.rrd&renderer=webgl) |
| 9 | [Rerun Viewer #9](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/9.rrd&renderer=webgl) |
| 10 | [Rerun Viewer #10](https://app.rerun.io/version/0.24.1/?url=https://storage.googleapis.com/zeroshot-public-rrds/puppet-high-res-rrd/10.rrd&renderer=webgl) |
### 📚 Citation
```bibtex
@dataset{zeroshot_tshirt_2025,
title = {ZeroShot SF-Fold Dataset},
author = {ZeroShot Data Team},
year = {2025},
note = {Version 1.2, September 2025},
url = {https://zeroshotdata.com/datasets/tshirt}
}
```