Improve model card for SARNN (RoboManipBaselines)

README.md

@@ -1,15 +1,22 @@
 ---
 license: bsd-2-clause
+pipeline_tag: robotics
 ---
+
 # Spatial attention recurrent neural network (SARNN)
 
-The model was trained with the [MujocoUR5eCable dataset](https://huggingface.co/datasets/RoboManipBaselines/MujocoUR5eCable).
+This repository contains a SARNN (Spatial attention recurrent neural network) model trained with the [MujocoUR5eCable dataset](https://huggingface.co/datasets/RoboManipBaselines/MujocoUR5eCable).
+
+The model is part of **RoboManipBaselines**, a unified framework for imitation learning in robotic manipulation across real and simulation environments.
+
+[[Paper](https://huggingface.co/papers/2509.17057)] [[Project Page](https://isri-aist.github.io/RoboManipBaselines-ProjectPage/)] [[GitHub](https://github.com/isri-aist/RoboManipBaselines)]
 
 ## Install
-See [GitHub](https://github.com/isri-aist/RoboManipBaselines/blob/master/doc/install.md#SARNN) for installation.
+See [GitHub](https://github.com/isri-aist/RoboManipBaselines/blob/master/doc/install.md#SARNN) for installation instructions.
 
 ## Policy rollout
-Run a trained policy:
+To run a trained policy, navigate to the top directory of the `robo_manip_baselines` repository and run:
+
 ```console
 # Go to the top directory of this repository
 $ cd robo_manip_baselines
@@ -17,8 +24,8 @@ $ python ./bin/Rollout.py Sarnn MujocoUR5eCable --checkpoint ./checkpoint/Sarnn/
 ```
 
 ## Technical Details
-For more information on the technical details, please see the following paper:
-```bib
+For more information on the technical details of the SARNN architecture, please see the following paper:
+```bibtex
 @INPROCEEDINGS{SARNN_ICRA2022,
   author = {Ichiwara, Hideyuki and Ito, Hiroshi and Yamamoto, Kenjiro and Mori, Hiroki and Ogata, Tetsuya},
   title = {Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility},
@@ -29,5 +36,14 @@ For more information on the technical details, please see the following paper:
 }
 ```
 
-## RoboManipBaselines Paper
-https://huggingface.co/papers/2509.17057
+## Citation
+If you use this framework or model in your work, please cite the RoboManipBaselines paper:
+
+```bibtex
+@article{RoboManipBaselines_Murooka_2025,
+  title={RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments},
+  author={Murooka, Masaki and Motoda, Tomohiro and Nakajo, Ryoichi and Oh, Hanbit and Makihara, Koshi and Shirai, Keisuke and Ogata, Tetsuya and Domae, Yukiyasu},
+  journal={arXiv preprint arXiv:2509.17057},
+  year={2025}
+}
+```