Datasets:

harpreetsahota
/

wirefishing-m

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              because column names don't match

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Dataset Card for WireFishing-M

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

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

dataset = load_from_hub("harpreetsahota/wirefishing-m")

session = fo.launch_app(dataset)

Dataset Details

Dataset Sources

Repository: Harvard Dataverse — WireFishing-M-1 · WireFishing-M-2 · Mendeley Data subset
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

Direct Use

WireFishing-M supports research in:

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 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

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

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

Data Collection and Processing

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).

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

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.

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.

Who are the source data producers?

Data was recorded by the authors at the University of Florida ICIC Lab. No third-party human subjects or social media data were involved.

Annotations

Annotation process

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?

Automated algorithm applied by the dataset authors. No human annotators.

Personal and Sensitive Information

The dataset contains no human subjects, biometric data, or personally identifiable information.

Citation

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:

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

The Mendeley subset (this FiftyOne dataset) contains 14 episodes. The full dataset is on Harvard Dataverse: WireFishing-M-1 and WireFishing-M-2.
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.