Add dataset card and paper link for MOTIF

README.md

@@ -0,0 +1,53 @@
+---
+task_categories:
+- robotics
+tags:
+- lerobot
+- cross-embodiment
+---
+
+# MOTIF: Learning Action Motifs for Few-shot Cross-Embodiment Transfer
+
+This repository contains a minimal real-world dataset provided to reproduce the interleaved task setting described in the paper [MOTIF: Learning Action Motifs for Few-shot Cross-Embodiment Transfer](https://huggingface.co/papers/2602.13764).
+
+[**GitHub**](https://github.com/buduz/MOTIF) | [**Paper**](https://huggingface.co/papers/2602.13764)
+
+## Dataset Description
+MOTIF is a framework for few-shot cross-embodiment robotic transfer. It learns reusable **action motifs**—embodiment-agnostic spatiotemporal patterns—that enable efficient policy generalization across different robot embodiments.
+
+This example dataset includes:
+- **Embodiments**: ARX5 and Piper.
+- **Tasks**: Two distinct tasks across embodiments.
+- **Format**: The dataset adheres to the [LeRobot](https://github.com/huggingface/lerobot) data format and includes a `modality.json` for detailed modality and annotation definitions (compatible with GR00T N1).
+
+## Usage
+
+### Download the Dataset
+You can download the dataset locally using the `huggingface-cli`:
+
+```bash
+huggingface-cli download \
+  --repo-type dataset Crossingz/ARX5_Piper_Few_shot_Example \
+  --local-dir ./demo_data
+```
+
+### Kinematic Trajectory Canonicalization
+To enable embodiment-agnostic motif learning, raw end-effector trajectories must be canonicalized into a shared reference frame. You can use the processing script provided in the [official repository](https://github.com/buduz/MOTIF):
+
+```bash
+python data/process/trajectory_canonicalization.py \
+  --dataset_path ./demo_data \
+  --save_path ./demo_data_processed
+```
+
+## Citation
+If you find this dataset or the MOTIF framework useful, please consider citing:
+
+```bibtex
+@article{zhi2025motif,
+  title={MOTIF: Learning Action Motifs for Few-shot Cross-Embodiment Transfer},
+  author={Zhi, Heng and Tan, Wentao and Zhu, Lei and Li, Fengling and Li, Jingjing and Yang, Guoli and Shen, Heng Tao},
+  journal={arXiv preprint arXiv:2602.13764},
+  year={2025}
+}
+```