🦞 TLabel-Bench

The First Cross-Sensor Unified Tactile Annotation Benchmark

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🎯 Why TLabel-Bench?

Every public tactile dataset today is sensor-specific — GelSight data only has GelSight format, DIGIT only DIGIT, DMA only DMA. No one has ever annotated the same set of objects across different sensors in a unified format that enables direct comparison.

TLabel-Bench fills this gap. It is the first benchmark that provides:

• Cross-sensor annotations for the same objects across GelSight Mini, DIGIT, DMA, and Xense
• Unified TLabel format — switch sensors by switching adapters, labels stay identical
• Multi-level annotations — material labels, episode segmentation (contact → press → release), and quality scores
• Three export formats — HDF5, CSV, and JSON for maximum compatibility

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📦 What's Included

tlabel-bench/
├── annotations/           ← TLabel annotation files (JSON/CSV)
│   ├── gelsight/          ← GelSight Mini annotations
│   ├── digit/             ← DIGIT annotations
│   ├── dma/               ← DMA (Daimon) annotations
│   └── xense/             ← Xense annotations
├── scripts/               ← Auto-download & merge scripts
├── evaluation/            ← Benchmark evaluation scripts
├── docs/                  ← Documentation
└── examples/              ← Quickstart notebooks

Note: This repository only contains annotation files and scripts. Raw sensor data must be downloaded separately using the provided scripts (see Data Sources).

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🚀 Quick Start

1. Install TLabel

pip install tlabel>=0.4.2

2. Download raw data

bash scripts/download_data.sh

3. Merge annotations with raw data

python scripts/merge_annotations.py

4. Run evaluation

python evaluation/material_classification.py

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📊 Annotation Schema

Each annotation file follows the TLabel standard schema:

Field	Type	Description
sensor_type	string	Sensor adapter name (e.g., "gelsight_mini")
object_id	string	Unique object identifier
material_label	string	Material category (rigid, soft, deformable)
episode	object	Episode segmentation: contact, press, release
quality_score	float	Data quality score (0.0–1.0)
timestamp	float	Relative timestamp in seconds

See annotation_schema.md for the full specification.

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🔬 Evaluation Tasks

Task	Metric	Description
Material Classification	Accuracy / F1	Classify objects by material using TLabel features
Episode Segmentation	IoU / F1	Detect contact→press→release boundaries
Cross-Sensor Transfer	Accuracy drop	Train on sensor A, test on sensor B
Quality Assessment	Correlation	Predict human quality ratings

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⚖️ Legal & Data Sources

This repository contains only annotation files (labels and metadata). Raw tactile data must be downloaded from their original sources. See data_sources.md for:

• Download links for each dataset
• License information
• Citation requirements

Why only annotations? Many tactile datasets do not have permissive licenses for redistribution. Our annotation files are original works under CC-BY-4.0, while raw data remains subject to their respective licenses.

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🔗 Related Projects

• TLabel — Sensor-agnostic tactile annotation toolkit (the tool that generates these annotations)
• Touch and Go — GelSight tactile-visual dataset
• SSVTP — DIGIT-based tactile pose estimation
• ObjTac — Multi-object tactile dataset

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📝 Citation

If you use TLabel-Bench in your research, please cite:

@dataset{tlabel_bench_2026,
  title={TLabel-Bench: The First Cross-Sensor Unified Tactile Annotation Benchmark},
  author={Wu, Sheng and Luo, Xi},
  year={2026},
  publisher={GitHub},
  url={https://github.com/liesliy/tlabel-bench}
}

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🤝 Contributing

We welcome contributions! See contribute.md for guidelines.

Areas we especially need help with:

• New sensor adapters (SynTouch, XELA, etc.)
• Additional objects and annotations
• Evaluation metrics and baselines

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📄 License

Annotation files are licensed under CC-BY-4.0. Raw data is subject to their respective original licenses.

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