🦞 TouchLabel AI
The World's First Sensor-Agnostic Tactile Data Annotation Toolkit
Load any tactile sensor → Annotate visually → Export a unified schema
GelSight · DIGIT · PaXini · Daimon — one tool, one format, all sensors
⚠️ v0.17.0 Breaking Change — Schema V2 Only. The legacy 22-dim
tlabel_v2format has been removed. All data now uses the 14-dim Schema V2 with Compliance Levels (L1-L4). See MIGRATION.md for upgrade instructions.
Tactile data shouldn't be locked inside any single company's format. Just as RGB images don't belong to any camera manufacturer, tactile data deserves a unified "Unicode". That's what TLabel does — defining a universal language for tactile data, and giving it to everyone.
🚀 Quick Start
pip install tlabel
import tlabel
# Load data from any sensor — auto-detected
data = tlabel.load("path/to/your/data")
# Or try built-in demo (30 seconds)
data = tlabel.demo("gelsight")
# Review in Jupyter — interactive annotation panel
data.review()
# Export to unified format
data.export("output.json") # JSON (tlabel_v2 schema)
data.export("output.csv") # CSV
data.export_ftp1("out.zarr") # FTP-1 Zarr (foundation model ready)
CLI Tools
tlabel version # Check version
tlabel list # List all registered adapters
tlabel info gelsight # Adapter details & capabilities
tlabel validate your_data.json # Validate tlabel_v2 schema compliance
🎯 What Does TLabel Do?
Problem: Every tactile sensor outputs different data formats. Switch sensors → rewrite code.
Solution: TLabel defines a unified 22-dimension feature space (tlabel_v2) and provides adapters that translate each sensor's native format into it.
GelSight .pkl ──┐ ┌── JSON (tlabel_v2)
PaXini .h5 ─────┤ TLabel Adapter ├── CSV
Daimon .parquet─┤ ──────────────► ├── FTP-1 Zarr
VTouch .h5 ─────┤ ├── LeRobot
Any format ─────┘ └── ROS2 (stub)
Core Capabilities
| Feature | Description |
|---|---|
| 🔌 9 Built-in Adapters | 7 dataset (file loading) + 2 real-time (SDK/USB) — open for community extensions |
| 🏗️ Open Platform | DataAdapterBase for datasets + SensorAdapterBase for live sensors — anyone can contribute |
| 🛠️ CLI Validation | tlabel validate checks your data against the 22-dim schema |
| 🤖 AI Pre-Annotation | PredictEngine auto-labels contact, slip, and manipulation phases |
| 📈 Data Augmentation | 5 methods (time_warp, noise, crop, scale, dropout), pure numpy, zero extra deps |
| 📤 Multi-Format Export | JSON, CSV, FTP-1 Zarr, LeRobot, RLDS, ROS2 |
| 🌐 Bilingual Panel | Interactive Jupyter annotation panel (中/EN) |
API at a Glance
# Loading
data = tlabel.load(path) # Auto-detect format
data = tlabel.load(path, format="paxini") # Force adapter
# Patching & Annotation
frame = data[0]
frame.patch("contact", 0) # Cascade rules auto-apply
data.batch_patch(10, 50, "slip_event", 1) # Range patch
# AI Pre-Annotation
from tlabel.predict import PredictEngine
engine = PredictEngine()
engine.fit(data) # Learn from partial labels
results = engine.predict(data)
engine.apply(data, results, min_confidence=0.7)
# Augmentation
augmented = tlabel.augment(data, methods=["time_warp", "noise_inject"], seed=42)
📖 Full API Reference → docs/API.md · 22-Dim Schema → docs/annotation-spec.md
📡 Supported Adapters
TLabel provides two types of adapters — loading existing data, or connecting live hardware.
Dataset Adapters — load existing tactile data
Base class:
DataAdapterBase· Input: file paths · Use case: research on public datasets, historical data processing
| Sensor | Type | File Format | Dims | Status |
|---|---|---|---|---|
| GelSight Mini / DIGIT | Vision-based | .pkl |
22 | ✅ Stable |
| Daimon DM-TacClaw | Multimodal | .parquet / dir |
22 | ✅ Stable |
| PaXini PXCap | Force array | .h5 / .hdf5 |
20 | ✅ Stable |
| UniVTAC | Vision-based | .hdf5 |
22 | ✅ Stable |
| TacQuad (AnyTouch) | Multi-sensor | directory | 22 | ✅ Stable |
| VTouch | Vision-based | .h5 |
22 | ✅ Stable |
| YCB-Slide | Vision-based | .npy / dir |
22 | ✅ Stable |
Real-time Sensor Adapters — connect live hardware
Base class:
SensorAdapterBase· Input: SDK / USB / stream · Use case: lab robots, production lines, real-time annotation
| Sensor | Type | Connection | Dims | Status |
|---|---|---|---|---|
| PaXini GEN3 | Force array | SDK (live stream) | 18 | 🆕 New |
| Daimon DM-Tac | Vision-based | USB / .avi / .bag |
22 | 🆕 Skeleton |
Which adapter should I build?
- I have recorded data files → inherit
DataAdapterBase, implement file parsing - I have a physical sensor → inherit
SensorAdapterBase, implement SDK/stream connection - Both share the same export pipeline — once registered,
tlabel.load()works automatically
Vision sensors → full 22 dims. Force-only sensors (PaXini) → 20 dims. No errors, no surprises — just graceful degradation.
# Per-sensor dependencies
pip install tlabel[gelsight] # opencv-python
pip install tlabel[paxini] # h5py
pip install tlabel[daimon] # pyarrow + opencv-python
pip install tlabel[all] # Everything
🆕 What's New
v0.16.0 — Open Platform Architecture
TLabel is now an open, extensible platform:
- 🏗️ Dual-base architecture (
DataAdapterBase/SensorAdapterBase) - 🔌 External adapter registration via
entry_points— third-party packages auto-discovered - 🛠️ CLI tools:
tlabel validate / list / info / version - 📦 Community contribution kit: templates + PR templates + CONTRIBUTING.md
📖 Full changelog → CHANGELOG_current.md
🤝 Contributing
TLabel is an open platform — anyone can extend it with new sensor support.
| Way | Effort | Impact |
|---|---|---|
Submit a PR — use contrib/adapter-template/, inherit from DataAdapterBase or SensorAdapterBase |
~30 min | Your sensor works with the whole ecosystem |
Ship an independent package — use tlabel entry_points for auto-discovery |
~1 hour | No PR needed, fully independent |
| Other — bug fixes, docs, tests, UI improvements | varies | Always welcome |
| Current Ecosystem | Count |
|---|---|
| Built-in adapters | 9 |
| Community adapters | 0 (your name here?) |
📖 Get started → CONTRIBUTING.md · Adapter Template → contrib/adapter-template/
🏆 Benchmark
TLabel-Bench — The first cross-sensor unified tactile annotation benchmark. Same objects, different sensors, one format.
📝 Citing TLabel
@software{tlabel2026,
title = {TLabel: A Sensor-Agnostic Tactile Data Annotation Toolkit},
author = {NiuZhu Tech},
year = {2026},
url = {https://github.com/liesliy/tlabel}
}
📄 License
MIT © NiuZhu Tech
If TLabel saved you from manually labeling tactile data, a ⭐ would make our day!
🤝 Need Help with Tactile Data?
We provide professional tactile data annotation and pipeline services:
- Custom sensor adapter development — integrate your sensor with TLabel in days
- Data pipeline consulting — annotation workflows for grasping, manipulation, slip detection
- Embodied AI tooling — end-to-end data solutions from raw sensor to model-ready datasets
Contact: WeChat wxid_olqx5z6trmtn21 · Email luoxi@touchlabelai.cn
1 maintainer
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