Update dataset card with relevant survey paper context

README.md

@@ -1,8 +1,20 @@
 ---
 license: apache-2.0
+task_categories:
+- robotics
+tags:
+- reinforcement-learning
+papers:
+- https://huggingface.co/papers/2508.13073
 ---
+
 # RLBench 18 Tasks Dataset
 
+This repository mirrors the RLBench dataset for 18 tasks, a benchmark frequently utilized and discussed in the field of robotic manipulation. This dataset is notably referenced in the survey paper [Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey](https://huggingface.co/papers/2508.13073), which provides a comprehensive overview of VLM-based Vision-Language-Action (VLA) models and the datasets supporting their development.
+
+The companion GitHub repository for the survey paper can be found here: [https://github.com/JiuTian-VL/Large-VLM-based-VLA-for-Robotic-Manipulation](https://github.com/JiuTian-VL/Large-VLM-based-VLA-for-Robotic-Manipulation).
+The official project page for the survey paper: [https://jiutian-vl.github.io/Awesome-VLA-for-Robotic-Manipulation/](https://jiutian-vl.github.io/Awesome-VLA-for-Robotic-Manipulation/)
+
 ## Overview
 This repository provides the RLBench dataset for 18 tasks, originally hosted by [PerAct](https://peract.github.io/) in Google Drive. Since downloading large files from Google Drive via terminal can be problematic due to various limits, we have mirrored the dataset on Hugging Face for easier access. To know more about the details of this dataset, please refer to [PerAct](https://peract.github.io/).
 
@@ -20,10 +32,10 @@ Each split contains zip files corresponding to individual tasks, making it easie
 ## Dataset Details
 These pre-generated RLBench demonstrations follow the same splits used in the original PerAct paper. Using these pre-generated demonstrations ensures reproducibility, as the original scene generation process involves randomness.
 
-- **Train Split:** 100 episodes per task
-- **Validation Split:** 25 episodes per task
-- **Test Split:** 25 episodes per task
-- **Total Size:** ~116GB
+-   **Train Split:** 100 episodes per task
+-   **Validation Split:** 25 episodes per task
+-   **Test Split:** 25 episodes per task
+-   **Total Size:** ~116GB
 
 ## Acknowledgements
 Thanks to the authors of PerAct and RLBench for creating and sharing the original dataset. This mirror aims to make data access more convenient for researchers.
@@ -40,7 +52,7 @@ If you use this dataset, please cite the original PerAct and RLBench papers:
 }
 
 @article{james2020rlbench,
-  title={Rlbench: The robot learning benchmark \& learning environment},
+  title={Rlbench: The robot learning benchmark & learning environment},
   author={James, Stephen and Ma, Zicong and Arrojo, David Rovick and Davison, Andrew J},
   journal={IEEE Robotics and Automation Letters},
   volume={5},
@@ -49,4 +61,4 @@ If you use this dataset, please cite the original PerAct and RLBench papers:
   year={2020},
   publisher={IEEE}
 }
-```
+```