---
license: cc-by-nc-4.0
task_categories:
- robotics
tags:
- tactile-sensing
- contact-fields
- manipulation
---

# SCFields Release Artifacts

This dataset repository contains assets, contact-field datasets, and contact-field checkpoints used by the SCFields release code.

**Paper:** [Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation](https://huggingface.co/papers/2602.13833)

**Project page:** https://kevinskwk.github.io/SCFields
**Code:** https://github.com/Kevinskwk/SCFields

## Layout

- `assets/tools`: generated TacSL tool assets.
- `assets/peeler_raw`: raw original peeler assets.
- `assets/peeler_combined`: combined peeler assets.
- `data/sim/tools`: simulated contact-field data for tools (`tools_mixed`).
- `data/sim/peelers`: simulated contact-field data for peelers (`peelers_combined_new`).
- `data/real/scraper`: converted real scraper contact-field data (`real_scraper_corrected_lambda1`).
- `checkpoints/contact_field/tools_sim`: tool contact-field checkpoint pretrained on simulated data.
- `checkpoints/contact_field/tools_real`: tool contact-field checkpoint finetuned on real data.
- `checkpoints/contact_field/peelers_sim`: peeler contact-field checkpoint pretrained on simulated data.
- `checkpoints/contact_field/peelers_real`: peeler contact-field checkpoint finetuned on real data.

Each checkpoint folder contains:

- `model.ckpt`: PyTorch Lightning checkpoint.
- `config.yaml`: the accompanying training configuration.

## Usage

### Download

You can use the Hugging Face CLI to download the artifacts:

```bash
hf download Kevinskwk/scfields-release \
  --repo-type dataset \
  --include "assets/**" \
  --include "data/sim/**" \
  --include "data/real/scraper/**" \
  --include "checkpoints/contact_field/**" \
  --local-dir /path/to/scfields
```

The release code's `scripts/download_assets.sh` maps shortened hosted asset paths to the repo-local asset layout expected by IsaacGym:

```text
assets/peeler_raw      -> assets/peeler
assets/peeler_combined -> assets/peelers_combined
```

### Training Example

To train the SCFields policy using the provided dataset and checkpoints, you can use the following command structure:

```bash
python train.py \
  --config-dir=config/scraping_real \
  --config-name=contact_field_delta_ee.yaml \
  data_root=/path/to/scfields \
  task.dataset.dataset_dir=/path/to/scfields/data/real/real_scraper_corrected_lambda1 \
  task.dataset.contact_field_checkpoint_path=/path/to/scfields/checkpoints/contact_field/tools_real/model.ckpt
```

## Citation

```bibtex
@misc{ma2026semanticcontactfieldscategorylevelgeneralizable,
    title={Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation},
    author={Kevin Yuchen Ma and Heng Zhang and Weisi Lin and Mike Zheng Shou and Yan Wu},
    year={2026},
    eprint={2602.13833},
    archivePrefix={arXiv},
    primaryClass={cs.RO},
    url={https://arxiv.org/abs/2602.13833},
}
```