Update README.md

README.md

@@ -1,3 +1,53 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- robotics
+---
+
+# MemoryBench Dataset
+
+MemoryBench is a benchmark dataset designed to evaluate spatial memory and action recall in robotic manipulation. This dataset accompanies the **SAM2Act+** framework, introduced in the paper *SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation*. For detailed task descriptions and more information about this paper, please visit SAM2Act's [website](https://sam2act.github.io).
+
+The dataset contains scripted demonstrations for three memory-dependent tasks designed in RLBench (same version as the one used in [PerAct](https://peract.github.io/)): 
+
+- **Reopen Drawer**: Tests 3D spatial memory along the z-axis.
+- **Put Block Back**: Evaluates 2D spatial memory along the x-y plane.
+- **Rearrange Block**: Requires backward reasoning based on prior actions.
+
+## Dataset Structure
+
+The dataset is organized as follows:
+```
+data/
+├── train/  # 100 episodes per task
+└── test/   # 25 episodes per task
+└── files/  # task files (.ttm & .py)
+```
+
+- **data/train/**: Contains three zip files, each corresponding to one of the three tasks. Each zip file contains **100** scripted demonstrations for training.
+- **data/test/**: Contains the same three zip files, but each contains **25** held-out demonstrations for evaluation.
+- **data/files/**: Includes necessary `.ttm` and `.py` files for running evaluation.
+
+## Usage
+
+This dataset is designed for use in the same manner as the RLBench 18 Tasks proposed by [PerAct](https://peract.github.io/). You can follow the same usage guidelines or stay updated with SAM2Act's [code repository](https://github.com/sam2act/sam2act) for further instructions.
+
+## Acknowledgement
+
+We would like to acknowledge [Haoquan Fang](https://hq-fang.github.io/) for leading the conceptualization of MemoryBench, providing key ideas and instructions for task design, and [Wilbert Pumacay](https://wpumacay.github.io/) for implementing the tasks and ensuring their seamless integration into the dataset. Their combined efforts, along with the oversight of [Jiafei Duan](https://duanjiafei.com/) and all co-authors, were essential in developing this benchmark for evaluating spatial memory in robotic manipulation.
+
+## Citation
+
+If you use this dataset, please cite the SAM2Act paper:
+
+```bibtex
+@misc{fang2025sam2act,
+      title={SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation}, 
+      author={Haoquan Fang and Markus Grotz and Wilbert Pumacay and Yi Ru Wang and Dieter Fox and Ranjay Krishna and Jiafei Duan},
+      year={2025},
+      eprint={2501.18564},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2501.18564}, 
+}
+```