Add full dataset card

README.md

@@ -1,89 +1,65 @@
 ---
-annotations_creators: []
-language: en
 license: cc-by-4.0
-size_categories:
-- n<1K
-task_categories: []
-task_ids: []
-pretty_name: wirefishing_m
+task_categories:
+  - robotics
 tags:
-- DIGIT-sensor
-- cable-insertion
-- contact-rich-manipulation
-- deformable-object-manipulation
-- fiftyone
-- franka-panda
-- multimodal
-- multimodal
-- robotics
-- tactile-sensing
-description: WireFishing-M is a multimodal robotics dataset for deformable cable insertion,
-  capturing a Franka Emika Panda 7-DOF arm with Allegro Robot Hand and DIGIT GelSight
-  tactile sensor inserting seven cable types into an L-shaped PVC pipe. Each of the
-  14 MCAP episodes contains four synchronized RGB camera streams and per-frame robot
-  state (force, torque, pose, joint angles, insertion success label).
-dataset_summary: '
-
-
-
-
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 14 samples.
-
+  - fiftyone
+  - multimodal
+  - robotics
+  - tactile-sensing
+  - cable-insertion
+  - deformable-object-manipulation
+  - contact-rich-manipulation
+  - franka-panda
+  - DIGIT-sensor
+pretty_name: WireFishing-M
+size_categories:
+  - n<1K
+dataset_summary: >-
+  WireFishing-M is a multimodal robotics dataset for deformable cable insertion,
+  capturing a Franka Emika Panda 7-DOF arm equipped with an Allegro Robot Hand
+  and DIGIT GelSight tactile sensor inserting seven types of cables into a
+  transparent L-shaped PVC pipe. Each episode contains synchronized streams from
+  four RGB cameras (bottom view, front view, pipe-side view, and tactile sensor)
+  plus per-frame robot state (end-effector force/torque, pose, joint angles, and
+  insertion success label). This FiftyOne dataset contains 14 MCAP episodes from
+  the Mendeley representative subset, structured as multimodal samples with five
+  synchronized channels per episode. The full dataset (≈3.9 million frames) is
+  available on Harvard Dataverse.
 
   ## Installation
 
-
-  If you haven''t already, install FiftyOne:
-
-
   ```bash
-
   pip install -U fiftyone
-
   ```
 
-
   ## Usage
 
-
   ```python
-
   import fiftyone as fo
-
   from fiftyone.utils.huggingface import load_from_hub
 
-
-  # Load the dataset
-
-  # Note: other available arguments include ''max_samples'', etc
-
   dataset = load_from_hub("harpreetsahota/wirefishing-m")
 
-
-  # Launch the App
-
   session = fo.launch_app(dataset)
-
   ```
-
-  '
 ---
 
-# Dataset Card for wirefishing_m
-
-<!-- Provide a quick summary of the dataset. -->
-
-
-
+# Dataset Card for WireFishing-M
 
-
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 14 samples.
+WireFishing-M is a multimodal robotics dataset for deformable cable insertion,
+capturing a Franka Emika Panda 7-DOF arm equipped with an Allegro Robot Hand
+and DIGIT GelSight tactile sensor inserting seven types of cables into a
+transparent L-shaped PVC pipe. Each episode contains synchronized streams from
+four RGB cameras (bottom view, front view, pipe-side view, and tactile sensor)
+plus per-frame robot state (end-effector force/torque, pose, joint angles, and
+insertion success label). This FiftyOne dataset contains 14 MCAP episodes from
+the Mendeley representative subset, structured as multimodal samples with five
+synchronized channels per episode. The full dataset (≈3.9 million frames) is
+available on Harvard Dataverse.
 
 ## Installation
 
-If you haven't already, install FiftyOne:
-
 ```bash
 pip install -U fiftyone
 ```
@@ -94,143 +70,218 @@ pip install -U fiftyone
 import fiftyone as fo
 from fiftyone.utils.huggingface import load_from_hub
 
-# Load the dataset
-# Note: other available arguments include 'max_samples', etc
 dataset = load_from_hub("harpreetsahota/wirefishing-m")
 
-# Launch the App
 session = fo.launch_app(dataset)
 ```
 
-
 ## Dataset Details
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
+- **Curated by:** Tianyu Zhou, Hengxu You, Fang Xu, Jing Du — Informatics, Cobots and Intelligent Construction (ICIC) Lab, University of Florida
+- **Funded by:** NVIDIA AI Technology Center (NVAITC) and UFIT under grant 00133684
+- **Shared by:** Tianyu Zhou, Hengxu You, Fang Xu, Jing Du
+- **Language(s):** Not applicable
+- **License:** CC BY 4.0
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** cc-by-4.0
+### Dataset Sources
 
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** Harvard Dataverse — [WireFishing-M-1](https://doi.org/10.7910/DVN/MUOJXI) · [WireFishing-M-2](https://doi.org/10.7910/DVN/XOPUXY) · Mendeley Data [subset](https://data.mendeley.com/datasets/64dsrcnhst/2)
+- **Paper:** Zhou et al., "WireFishing-M: A multimodal dataset for deformable cable insertion using tactile, visual, and proprioceptive sensing," *Data in Brief*, vol. 63, Dec. 2025, 112136. https://doi.org/10.1016/j.dib.2025.112136
+- **Demo:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
+WireFishing-M supports research in:
 
-[More Information Needed]
+- **Multimodal perception and sensor fusion** — the four synchronized camera streams (bottom, front, pipe-side, tactile) and robot state provide rich input for early and late fusion architectures.
+- **Insertion success detection** — automated binary success labels derived from pipe-side camera pixel counts enable training and evaluation of task-completion classifiers.
+- **Force estimation and contact modeling** — end-effector force/torque time-series paired with tactile images support data-driven contact estimation.
+- **Manipulation policy learning** — human demonstration trials with higher success rates and randomized trials with diverse failure modes cover both imitation learning and reinforcement learning scenarios.
+- **Generalization across cable types** — seven cables varying in stiffness, diameter, surface texture, and weight support transfer learning and domain adaptation studies.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+This dataset was collected in a fixed laboratory environment using a single pipe geometry. It is not suitable as-is for benchmarking manipulation in unstructured environments or with pipe geometries other than the 1″ Sch 40 L-shaped PVC assembly used during collection.
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+### Overview
+
+This FiftyOne dataset contains **14 MCAP episodes** (samples), one per trial in the Mendeley representative subset. Each sample's `media_type` is `multimodal`. Episodes are authored at 5 fps from the raw per-frame files; timestamps are synthesized as `frame_index × 200 ms` because the original ROS timestamps are not included in the Mendeley export.
+
+The full WireFishing-M dataset on Harvard Dataverse contains approximately 3.9 million randomized-trial frames and 44,288 human-demonstration frames across multiple date-stamped sessions per cable type. The Mendeley subset provides one trial per cable per condition (14 total), with 675–1,000 frames per episode.
+
+### MCAP Channel Structure
+
+Each MCAP episode contains nine synchronized channels (all protobuf):
+
+| Topic | Schema | Tile | Resolution | Notes |
+|-------|--------|------|-----------|-------|
+| `/camera/global/image_raw` | `foxglove.CompressedImage` | Image | 1280 × 720 | Bottom view (Azure Kinect). Folder on disk: `globel_view/` (dataset typo) |
+| `/camera/local/image_raw` | `foxglove.CompressedImage` | Image | 1920 × 1080 | Front view (Femto Mega). Paper states 1280 × 720; actual files are 1920 × 1080 |
+| `/camera/inner/image_raw` | `foxglove.CompressedImage` | Image | 640 × 480 | Pipe-side view (USB endoscope) |
+| `/camera/tactile/image_raw` | `foxglove.CompressedImage` | Image | 240 × 960 | Three DIGIT fingertips stacked vertically (index top, thumb middle, middle finger bottom). Paper states grayscale; files are RGB JPEG |
+| `/robot/ee_force` | `foxglove.Vector3` | Plot | — | End-effector force (x=Fx, y=Fy, z=Fz) in N |
+| `/robot/ee_torque` | `foxglove.Vector3` | Plot | — | End-effector torque (x=Tx, y=Ty, z=Tz) in N·m |
+| `/robot/ee_position` | `foxglove.Vector3` | Plot | — | End-effector position (x, y, z) in m |
+| `/robot/ee_orientation` | `foxglove.Vector3` | Plot | — | End-effector orientation (x=roll, y=pitch, z=yaw) in rad. Note: paper describes quaternion; empirical values indicate Euler RPY |
+| `/robot/joint_states` | `foxglove.JointStates` | Plot | — | 7 Panda joint angles (`joint_1`…`joint_7`, `position` field in rad) plus `success` joint (`position` = 1.0 for successful insertion, 0.0 for failure) |
+
+### Sample-Level Fields
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `filepath` | `StringField` | Absolute path to the `.mcap` episode file |
+| `cable_id` | `IntField` | Cable number (1–7), inferred from folder name |
+| `condition` | `StringField` | Insertion condition: `"human"` (manually guided, higher success rate) or `"random"` (randomized start pose, diverse failure modes) |
+| `trial` | `IntField` | Trial number within the cable/condition folder (always `1` in this Mendeley subset) |
+| `has_robot_data` | `BooleanField` | `True` if the episode contains robot state channels. All 14 episodes in this subset have robot data |
+| `size_mb` | `FloatField` | MCAP file size in megabytes (342–525 MB per episode) |
+| `n_frames` | `IntField` | Number of synchronized frames in the episode |
+| `n_success_frames` | `IntField` | Number of frames where `insertion_success == 1` |
+| `success_rate` | `FloatField` | Fraction of frames labeled as successful insertion |
+| `max_force_magnitude` | `FloatField` | Maximum end-effector force magnitude (N) across all frames: `max(√(Fx²+Fy²+Fz²))` |
+| `mean_force_magnitude` | `FloatField` | Mean end-effector force magnitude (N) across all frames |
+| `max_torque_magnitude` | `FloatField` | Maximum end-effector torque magnitude (N·m) across all frames: `max(√(Tx²+Ty²+Tz²))` |
+| `ee_pos_range_x` | `FloatField` | Range of end-effector x-position across the episode (m) |
+| `ee_pos_range_y` | `FloatField` | Range of end-effector y-position across the episode (m) |
+| `ee_pos_range_z` | `FloatField` | Range of end-effector z-position across the episode (m) |
+| `clip_embedding` | `VectorField` | 512-dim CLIP ViT-B/32 embedding of the middle-frame global camera image, L2-normalized. Computed from `openai/clip-vit-base-patch32` via Hugging Face Transformers |
+| `qwen3vl_embedding` | `VectorField` | 6144-dim video embedding: Qwen3-VL-Embedding-8B `embed_frames` applied independently to 200 sampled frames (stride 5) from each of three camera channels (global, local, inner), then concatenated and L2-normalized. Captures temporal dynamics across three viewpoints simultaneously |
+
+### Brain Runs
+
+| Brain key | Method | Description |
+|-----------|--------|-------------|
+| `clip_umap` | UMAP (2D) | Dimensionality reduction of `clip_embedding` for visual exploration of episode similarity in the FiftyOne App Embeddings panel |
+| `qwen3vl_umap` | UMAP (2D) | Dimensionality reduction of `qwen3vl_embedding` — multiview video-aware episode similarity |
+
+### Robot State Fields (source `.npy` mapping)
+
+| MCAP channel | Field path | Source indices | Units | Notes |
+|--------------|-----------|---------------|-------|-------|
+| `/robot/ee_force` | `.x`, `.y`, `.z` | [0–2] | N | End-effector force Fx, Fy, Fz |
+| `/robot/ee_torque` | `.x`, `.y`, `.z` | [3–5] | N·m | End-effector torque Tx, Ty, Tz |
+| `/robot/ee_position` | `.x`, `.y`, `.z` | [6–8] | m | End-effector Cartesian position |
+| `/robot/ee_orientation` | `.x`, `.y`, `.z` | [9–11] | rad | End-effector orientation (roll, pitch, yaw). Paper describes quaternion; empirical values indicate Euler RPY |
+| `/robot/joint_states` | `joints[0–6].position` | [12–18] | rad | Franka Emika Panda joint angles (7-DOF) |
+| `/robot/joint_states` | `joints[7].position` (name=`success`) | [19] | — | Binary insertion label: `1.0` = success, `0.0` = failure |
+
+To plot force x in the FiftyOne viewer: add series `/robot/ee_force.x`. For joint angles: `/robot/joint_states.joints[0].position` through `joints[6].position`.
+
+Indices [20–33] of the raw `.npy` arrays are not logged (zero for all episodes in this subset; not described in the paper).
+
+### Cable Types
+
+| `cable_id` | Description | Full-dataset random frames | Full-dataset human frames |
+|-----------|-------------|--------------------------|--------------------------|
+| 1 | Flexible, Thin, Lightweight, Braided nylon cover | ≈2,900,000 | 5,962 |
+| 2 | Rigid, Thick, Heavy-duty, No cover | 84,621 | 5,994 |
+| 3 | Rigid, Thin, Lightweight, No cover | 57,285 | 6,668 |
+| 4 | Less Flexible, Thin, Lightweight, Braided nylon cover | 169,602 | 6,170 |
+| 5 | Less Rigid, Thin, Lightweight, No cover | 284,262 | 7,624 |
+| 6 | Rigid, Thin, Lightweight, Braided nylon cover | 27,232 | 4,949 |
+| 7 | Flexible, Thin, Lightweight, No cover | 384,475 | 7,141 |
+
+### Mendeley Subset Frame Counts (this FiftyOne dataset)
+
+| `cable_id` | `condition` | Frames | MCAP size |
+|-----------|-------------|--------|-----------|
+| 1 | human | 1,000 | 471 MB |
+| 1 | random | 1,000 | 474 MB |
+| 2 | human | 914 | 450 MB |
+| 2 | random | 1,000 | 521 MB |
+| 3 | human | 675 | 342 MB |
+| 3 | random | 1,000 | 506 MB |
+| 4 | human | 1,000 | 493 MB |
+| 4 | random | 1,000 | 510 MB |
+| 5 | human | 1,000 | 480 MB |
+| 5 | random | 1,000 | 524 MB |
+| 6 | human | 755 | 369 MB |
+| 6 | random | 1,000 | 524 MB |
+| 7 | human | 839 | 408 MB |
+| 7 | random | 1,000 | 525 MB |
+
+### Splits
+
+No train/val/test split is provided. Filter by `condition` (`"human"` / `"random"`) or `cable_id` (1–7) using FiftyOne views.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+WireFishing-M was created to address the lack of multimodal benchmarks for deformable object manipulation in contact-rich environments. The wire insertion task is representative of real-world scenarios in construction, electrical assembly, and robotics. Collecting synchronized tactile, visual, proprioceptive, and force data across seven cable types and two collection conditions (human demonstration and randomized robot insertions) provides a resource covering both expert-like behavior and diverse failure modes.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+Data was collected at the ICIC Lab, University of Florida (1949 Stadium Rd, Weil Hall 360, Gainesville, FL 32611). A Franka Emika Panda 7-DOF manipulator on a Vention workstation was equipped with an Allegro Robot Hand (16-DOF) and a DIGIT GelSight tactile sensor on the index, middle, and thumb fingertips. The insertion target was a transparent L-shaped PVC pipe (1″ Sch 40 NSF-61).
 
-[More Information Needed]
+Sensors and recording rates:
+- **Front camera**: Femto Mega RGB-D, 1280 × 720 px, 5 fps
+- **Bottom camera**: Microsoft Azure Kinect DK, 1280 × 720 px, 5 fps
+- **Pipe-side camera**: 16.4 ft USB endoscope, 640 × 480 px, 5 fps
+- **Tactile sensor**: DIGIT GelSight on three fingertips, concatenated to 240 × 960 px, ≈5 fps
+- **Robot state**: Franka ROS interface (`/franka_state_controller/franka_states`), 5 fps
 
-#### Who are the source data producers?
+All streams were timestamped via ROS Melodic on Ubuntu 18.04 and synchronized offline using software timestamps. No hardware synchronization was applied. Camera intrinsic and extrinsic parameters were calibrated with a checkerboard pattern; calibration files are not included in the dataset. Raw data was post-processed into per-frame `.jpg` and `.npy` files.
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+For randomized trials, the robot moved to a randomized initial pose and then applied incremental forward steps (0–2 cm along x-axis) with simultaneous small adjustments in y/z (±1 cm) and random pitch/yaw (±15°), resetting when a predefined insertion depth was exceeded. For human demonstration trials, a human physically guided the robot end-effector through the insertion.
 
-[More Information Needed]
+#### Who are the source data producers?
 
-### Annotations [optional]
+Data was recorded by the authors at the University of Florida ICIC Lab. No third-party human subjects or social media data were involved.
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Annotations
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
+Insertion success was labeled automatically. The pipe-side camera image was converted to grayscale; if the pixel count below intensity 50 in a predefined region near the pipe outlet exceeded 40 pixels, the trial frame was labeled as a successful insertion (label = 1), otherwise as failure (label = 0). This labeling is stored as index [19] in the `.npy` robot state array and exposed as `insertion_success` in the `/robot/state` MCAP channel.
 
 #### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+Automated algorithm applied by the dataset authors. No human annotators.
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
+The dataset contains no human subjects, biometric data, or personally identifiable information.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article{zhou2025wirefishing,
+  title   = {WireFishing-M: A multimodal dataset for deformable cable insertion using tactile, visual, and proprioceptive sensing},
+  author  = {Zhou, Tianyu and You, Hengxu and Xu, Fang and Du, Jing},
+  journal = {Data in Brief},
+  volume  = {63},
+  pages   = {112136},
+  year    = {2025},
+  doi     = {10.1016/j.dib.2025.112136}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
+Zhou, T., You, H., Xu, F., & Du, J. (2025). WireFishing-M: A multimodal dataset for deformable cable insertion using tactile, visual, and proprioceptive sensing. *Data in Brief*, 63, 112136. https://doi.org/10.1016/j.dib.2025.112136
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+- The Mendeley subset (this FiftyOne dataset) contains 14 episodes. The full dataset is on Harvard Dataverse: [WireFishing-M-1](https://doi.org/10.7910/DVN/MUOJXI) and [WireFishing-M-2](https://doi.org/10.7910/DVN/XOPUXY).
+- Raw per-frame files in the Mendeley download use non-sequential frame indices. The `_N` suffix across all five subfolders within a trial is the synchronization key: all files named `*_N.*` for the same `N` are co-temporal.
+- The bottom-camera folder is named `globel_view` (typo) in the downloaded dataset, not `global_view` as described in the paper.
+- The raw `.npy` robot state arrays have shape `(34,)`. Indices [20–33] are zero in the Mendeley subset and are not documented in the paper.
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors
 
-[More Information Needed]
+Harpreet Sahota
 
 ## Dataset Card Contact
 
-[More Information Needed]
+[More Information Needed]