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Dataset

Pair Touch 13m

by BorisGuo ID: hf-dataset--borisguo--pair_touch_13m

FNI Rank 50

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Multi-modal tactile dataset with pose, force, and tactile sensor data. | Config | Description | Sensors | |--------|-------------|---------| | | Pose estimation data | tac02/xela + camera | | | Force measuremen...

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Data Integrity 50 FNI Score

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Dataset Information Summary
Entity Passport
Registry ID	hf-dataset--borisguo--pair_touch_13m
Provider	huggingface

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Cite this dataset

Academic & Research Attribution

BibTeX

@misc{hf_dataset__borisguo__pair_touch_13m,
  author = {BorisGuo},
  title = {Pair Touch 13m Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/BorisGuo/pair_touch_13m}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

BorisGuo. (2026). Pair Touch 13m [Dataset]. Free2AITools. https://huggingface.co/datasets/BorisGuo/pair_touch_13m

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AI Nexus Index

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50.0

Top 0% Overall Impact

🔥 Popularity (P) 0

🚀 Velocity (V) 0

🛡️ Credibility (C) 0

🔧 Utility (U) 0

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The Nexus Index for Pair Touch 13m aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

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📊 P: HuggingFace Stats 📈 V: 7-Day Delta 📄 C: Papers With Code 🔧 U: Deploy Score

Data Verified 🕐 Last Updated: Not calculated

Free2AI Nexus Index | Fair · Transparent · Explainable | Full Methodology

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89,218

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Dataset Specification

configs:

config_name: pose_data
data_files: "pose_data/metadata.jsonl"
config_name: force_data
data_files: "force_data/metadata.jsonl"
config_name: tacniq_gsmini
data_files: "tacniq_gsmini/metadata.jsonl"
config_name: xela_9dtact
data_files: "xela_9dtact/metadata.jsonl"

PairTouch 13M Dataset

Multi-modal tactile dataset with pose, force, and tactile sensor data.

Configs

Config	Description	Sensors
`pose_data`	Pose estimation data	tac02/xela + camera
`force_data`	Force measurement data	tac02/xela + gelsight
`tacniq_gsmini`	TacNIQ + GSMini data	tacniq + gsmini
`xela_9dtact`	XELA + 9DTact data	xela + 9dtact

Usage

from datasets import load_dataset

Load specific configds = load_dataset("BorisGuo/pair_touch_13m", "pose_data")

Tactile Sensors

tac02 / tacniq

Taxel layout: 11 x 6 = 66 taxels
Force type: Z-axis normal force only
Dead taxels: Index 0 (top-left) and 5 (top-right) always read 0, should be ignored

Taxel indices:
[ 0][ 1][ 2][ 3][ 4][ 5]   <- Row 0 (index 0, 5 are dead)
[ 6][ 7][ 8][ 9][10][11]   <- Row 1
...
[60][61][62][63][64][65]   <- Row 10

xela

Taxel layout: 4 x 6 = 24 taxels
Force type: 3D force (X, Y, Z axes), total 72 values (24 x 3)
Data format: Flattened as [x0, y0, z0, x1, y1, z1, ..., x23, y23, z23]

Sampling Frequency

force_data (1 tactile sample per frame)

Dataset	Frame Rate	Tactile Sensor
9dtact_force_h5	~10 Hz	-
gelsight_force_h5	~25 Hz	-
tac02_force_h5	~200 Hz	tac02
xela_force_h5	~100 Hz	xela

pose_data (multiple tactile samples per frame)

Dataset	Frame Rate	Samples/Frame	Effective Tactile Rate
tac02_pose_h5	~7.5 Hz	20	~150 Hz
xela_pose_h5	~7.5 Hz	10	~75 Hz
gelsight_pose_h5	~7.5 Hz	-	-
9dtact_pose_h5	~7.5 Hz	-	-

Paired sensor data

Dataset	Frame Rate	Sensors
tacniq_gsmini	10 Hz	tacniq + gsmini
xela_9dtact	10 Hz	xela + 9dtact

Data Fields

file_name: Path to image file
episode_id: Episode identifier
frame_idx: Frame index within episode
timestamp: Timestamp
rotation: 3D rotation
translation: 3D translation
tactile: tac02 sensor matrix (11x6 taxels, Z-axis only)
xela: XELA sensor matrix (4x6x3 taxels, 3D force)
tacniq: TacNIQ sensor matrix (same as tac02)
video: Episode video path

Data Dimensions

images: (N, S, 224, 224, 3) - N frames, S samples per frame
tactile (pose_data): (N, 20, 66) - N frames, 20Hz sampling, 66 taxels (11x6)
xela (pose_data): (N, 10, 72) - N frames, 10Hz sampling, 72 values (4x6x3)
tactile/xela (force_data): (N, 66) or (N, 72) - 1 sample per frame

Preprocessing

Use preprocess.py for data processing:

# Parse H5 files to JSON/images python preprocess.py extract Check H5 structure (without parsing) python preprocess.py extract --check Update metadata (add heatmap/video paths) python preprocess.py extract --update Generate heatmaps python preprocess.py heatmap python preprocess.py heatmap --test # Test mode python preprocess.py heatmap --type tac02_pose # Specific type Generate xela marker flow (point displacement visualization) python preprocess.py marker_flow python preprocess.py marker_flow --test python preprocess.py marker_flow --type xela_pose Generate videos python preprocess.py video python preprocess.py video --test Pack images into tar files (reduce file count for HF upload) python preprocess.py pack # Pack only python preprocess.py pack --delete # Pack and delete original PNGs Unpack images from tar files python preprocess.py unpack # Unpack only python preprocess.py unpack --delete # Unpack and delete tar files Clean up (delete all PNGs, keep only videos) python preprocess.py clean Upload to Hugging Face python preprocess.py upload # 只上传/更新，不删除远端文件 python preprocess.py upload --sync # 同步模式：删除远端存在但本地不存在的文件 Full pipeline (extract -> heatmap -> video -> update)

python preprocess.py all

Heatmap Types

Type	Description
`tac02_pose`	pose_data/tac02_pose_h5
`xela_pose`	pose_data/xela_pose_h5
`pose`	All pose_data
`tac02_force`	force_data/tac02_force_h5
`xela_force`	force_data/xela_force_h5
`force`	All force_data
`tacniq_gsmini`	tacniq_gsmini
`xela_9dtact`	xela_9dtact
`all`	Everything

Video Types

Type	Description
`9dtact_force`	force_data/9dtact_force_h5
`xela_force`	force_data/xela_force_h5
`gelsight_force`	force_data/gelsight_force_h5
`tac02_force`	force_data/tac02_force_h5
`gelsight_pose`	pose_data/gelsight_pose_h5
`9dtact_pose`	pose_data/9dtact_pose_h5
`tac02_pose`	pose_data/tac02_pose_h5
`xela_pose`	pose_data/xela_pose_h5
`xela_pose_flow`	pose_data/xela_pose_h5 marker_flow (video_flow.mp4)
`xela_force_flow`	force_data/xela_force_h5 marker_flow (video_flow.mp4)
`xela_9dtact_flow`	xela_9dtact marker_flow (video_flow.mp4)
`tacniq_gsmini`	tacniq_gsmini (video_gsmini.mp4 + video_tacniq.mp4)
`xela_9dtact`	xela_9dtact (video_9dtact.mp4 + video_xela.mp4)
`all`	Everything

Top Tier

Social Proof

HuggingFace Hub

2Likes

89.2KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

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AI Summary: Based on Hugging Face metadata. Not a recommendation.

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🛡️ Dataset Transparency Report

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🆔 Identity & Source

id: hf-dataset--borisguo--pair_touch_13m
source: huggingface
author: BorisGuo
tags: modality:imageregion:us

⚙️ Technical Specs

architecture: null
params billions: 0.013
context length: null

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likes: 2
downloads: 89,218

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