JulienRineau/human_folds_v30_256

Zeroshot Tshirt

Real-world data for general robotics.

🤖 What is Zeroshot Tshirt?

This dataset contains real-world residential t-shirt folding demonstrations, collected by trained data collectors using hand-held grippers in diverse home environments.

📖 Table of Contents

1. Features
2. Terminology
3. Specifications
   • Dataset Composition
   • Environment Composition
   • Garment Composition
   • Trajectory Specifications
   • Hardware Specifications
   • Physical Specifications
4. Data Format
   • Conventions
   • Directory Layout
   • Video Files
   • Parquet Format
   • Metadata Files
   • Frame of Reference
5. Collection
   • Collection Info
   • Privacy & Consent
   • T-Shirt Folding Procedure Guidelines
   • T-Shirt Folding Instructions
6. Preprocessing & Annotation
7. Validation
8. Distribution
9. 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

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

Frame Values

• T_puppet_imu (origin)

  [[ 1.        0.        0.        0.01625 ]
    [ 0.        1.        0.       -0.016424]
    [ 0.        0.        1.        0.189372]
    [ 0.        0.        0.        1.      ]]
  

• T_joint_axis (origin to end actuator mounting location)

  [[ 1.        0.        0.        0.01685 ]
    [ 0.        1.        0.       -0.032924]
    [ 0.        0.        1.       -0.03476 ]
    [ 0.        0.        0.        1.      ]]
  

• T_normal_fingertip (origin to end actuator fingertip)

  [[ 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)

  [[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 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

    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
left_last_joint_pose	list[float32]	m, quat	length 7	3D pose of left puppet “motion axis” joint in world frame
right_last_joint_pose	list[float32]	m, quat	length 7	3D pose of right puppet “motion axis” joint 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

"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_last_joint_pose": [0.456, -0.112, 0.622, 0.011, 0.511, 0.002, 0.859],
"right_last_joint_pose": [0.461, 0.117, 0.624, -0.010, 0.515, -0.004, 0.857],
"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]

Metadata Files

• info.json — Global dataset metadata, including:

  • codebase_version (e.g., v2.1)
  • robot_type (e.g., zeroshot)
  • Episode and frame counts (total_episodes, total_frames)
  • Chunking details (total_chunks, chunk_size)
  • Data and video path templates
  • Global features schema (per-signal dtype, shape, pose keys, video codec info, etc.)

• tasks.jsonl — Line-delimited JSON defining tasks:

  • task_id
  • name

• episodes.jsonl — Line-delimited JSON describing episodes:

  • episode_id / episode_index
  • Associated task_id

• episodes_stats.jsonl — Line-delimited JSON with per-episode statistics:

  • episode_id / episode_index
  • num_frames
  • duration (seconds)
  • Error flags (e.g., missing frames, dropped sensors)

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.

   

4. Create folds
   a. Fold the shirt upward from the bottom to the midline.

   

   b. Fold again from the midline to the collar to form a compact rectangle.

   

5. Stack
   a. Place the folded shirt neatly onto the prepared stack or storage area.

   

✒️ 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.)
• Contextual comments and anomalies
• Optional fine-grained sub-task labels available

🔬 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 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.
These results confirm the reliability and consistency of positional tracking across diverse motion patterns.

Orientation Accuracy

Orientation estimates were validated against quaternion data from the OptiTrack system.
A double Butterworth filter was applied to the raw rotational signals, effectively reducing jitter while preserving sharp transitions.
Across all validation trials, the system achieved sub-degree median orientation errors, demonstrating fine angular precision suitable for high-accuracy manipulation tasks.

Metric	Accuracy
3D Position	≤ 10 mm error
Orientation	≤ 1° error

🚚 Distribution

ZeroShot is responsible for maintaining this dataset. For any questions or concerns, please contact interest@zeroshotdata.com.

The dataset will be released as an open-source resource under the Apache 2.0 license and made publicly available via a public Google Cloud Storage Bucket.
A planned public release is scheduled, with subsequent updates distributed through the same platform.
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.

Licensing and terms of use are currently under review (expected to align with a modified BSD 3-Clause license where redistribution may require permission).
Any restrictions on commercial use, redistribution, or derivative works will be specified once finalized.

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

Interactive Samples

Sample	Link
1	Rerun Viewer #1
2	Rerun Viewer #2
3	Rerun Viewer #3
4	Rerun Viewer #4
5	Rerun Viewer #5
6	Rerun Viewer #6
7	Rerun Viewer #7
8	Rerun Viewer #8
9	Rerun Viewer #9
10	Rerun Viewer #10

📚 Citation

@dataset{zeroshot_tshirt_2025,
  title     = {ZeroShot T-shirt Folding Dataset},
  author    = {ZeroShot Data Team},
  year      = {2025},
  note      = {Version 1.2, September 2025},
  url       = {https://zeroshotdata.com/datasets/tshirt}
}