Add SAM2Act replay buffer README

README.md

@@ -1,3 +1,52 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- robotics
+library_name: robotics
+tags:
+- sam2act
+- replay-buffer
+- robot-manipulation
+- rlbench
+- memorybench
+---
+
+# SAM2Act
+
+SAM2Act is a multi-view robotics transformer policy for robotic manipulation. Built on RVT-2, it combines multi-resolution upsampling with visual embeddings from the SAM2 foundation model to improve 3D action prediction, multitask learning, and generalization. SAM2Act+ extends this policy with a memory bank, memory encoder, and memory attention so the agent can condition on prior observations and actions for spatial memory-dependent tasks.
+
+For full project details, code, training instructions, and videos, see the [SAM2Act website](https://sam2act.github.io/) and [GitHub repository](https://github.com/sam2act/sam2act).
+
+## Replay Buffers
+
+This dataset repository stores pre-generated replay buffers for training SAM2Act and SAM2Act+. The buffers are serialized YARR replay buffers generated from the RLBench and MemoryBench demonstrations, so they can be loaded directly during training instead of being rebuilt on the fly.
+
+The repository is organized as follows:
+
+```text
+replay_temporal/replay_train/          # RLBench 18-task replay buffers
+replay_temporal_memory/replay_train/   # MemoryBench replay buffers
+```
+
+Each task is provided as a `.tar.xz` archive. After downloading, extract each archive with `tar -xf <task_name>.tar.xz` and place the extracted task folders under the matching local directory in the SAM2Act codebase:
+
+- RLBench: `sam2act/sam2act/replay_temporal/replay_train`
+- MemoryBench: `sam2act/sam2act/replay_temporal_memory/replay_train`
+
+These replay buffers are intended for training from scratch. They are not required for evaluating the pretrained models in [hqfang/sam2act-models](https://huggingface.co/hqfang/sam2act-models).
+
+## Bibtex
+
+If you use these replay buffers, 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},
+}
+```