| --- |
| 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}, |
| } |
| ``` |
|
|
