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license: mit |
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task_categories: |
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- robotics |
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--- |
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<div align="center"> |
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<h2>Magma: A Foundation Model for Multimodal AI Agents</h2> |
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[Jianwei Yang](https://jwyang.github.io/)<sup>*</sup><sup>1</sup><sup>†</sup> |
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[Reuben Tan](https://cs-people.bu.edu/rxtan/)<sup>1</sup><sup>†</sup> |
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[Qianhui Wu](https://qianhuiwu.github.io/)<sup>1</sup><sup>†</sup> |
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[Ruijie Zheng](https://ruijiezheng.com/)<sup>2</sup><sup>‡</sup> |
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[Baolin Peng](https://scholar.google.com/citations?user=u1CNjgwAAAAJ&hl=en&oi=ao)<sup>1</sup><sup>‡</sup> |
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[Yongyuan Liang](https://cheryyunl.github.io)<sup>2</sup><sup>‡</sup> |
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[Yu Gu](http://yu-gu.me/)<sup>1</sup> |
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[Mu Cai](https://pages.cs.wisc.edu/~mucai/)<sup>3</sup> |
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[Seonghyeon Ye](https://seonghyeonye.github.io/)<sup>4</sup> |
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[Joel Jang](https://joeljang.github.io/)<sup>5</sup> |
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[Yuquan Deng](https://scholar.google.com/citations?user=LTC0Q6YAAAAJ&hl=en)<sup>5</sup> |
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[Lars Liden](https://sites.google.com/site/larsliden)<sup>1</sup> |
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[Jianfeng Gao](https://www.microsoft.com/en-us/research/people/jfgao/)<sup>1</sup><sup>▽</sup> |
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<sup>1</sup> Microsoft Research; <sup>2</sup> University of Maryland; <sup>3</sup> University of Wisconsin-Madison |
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<sup>4</sup> KAIST; <sup>5</sup> University of Washington |
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<sup>*</sup> Project lead <sup>†</sup> First authors <sup>‡</sup> Second authors <sup>▽</sup> Leadership |
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\[[arXiv Paper](https://www.arxiv.org/pdf/2502.13130)\] \[[Project Page](https://microsoft.github.io/Magma/)\] \[[Hugging Face Paper](https://huggingface.co/papers/2502.13130)\] \[[Github Repo](https://github.com/microsoft/Magma)\] \[[Video](https://www.youtube.com/watch?v=SbfzvUU5yM8)\] |
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</div> |
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## Introduction |
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This dataset contains the robotic manipulation data used in Magma pretraining. For fair comparison, we followed OpenVLA to use the data mix "siglip-224px+mx-oxe-magic-soup". |
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The dataset is organized by following source datasets, with each source containing one or more arrow files: |
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| Folder | Number of Shards | |
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|:------------------------------------------------------|-------------------:| |
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| austin_buds_dataset_converted_externally_to_rlds | 1 | |
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| austin_sailor_dataset_converted_externally_to_rlds | 4 | |
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| austin_sirius_dataset_converted_externally_to_rlds | 3 | |
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| berkeley_autolab_ur5 | 1 | |
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| berkeley_cable_routing | 1 | |
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| berkeley_fanuc_manipulation | 1 | |
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| bridge_orig | 17 | |
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| cmu_stretch | 1 | |
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| dlr_edan_shared_control_converted_externally_to_rlds | 1 | |
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| fractal20220817_data | 21 | |
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| furniture_bench_dataset_converted_externally_to_rlds | 4 | |
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| iamlab_cmu_pickup_insert_converted_externally_to_rlds | 2 | |
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| jaco_play | 1 | |
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| kuka | 21 | |
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| language_table | 8 | |
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| nyu_franka_play_dataset_converted_externally_to_rlds | 1 | |
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| roboturk | 3 | |
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| stanford_hydra_dataset_converted_externally_to_rlds | 4 | |
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| taco_play | 3 | |
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| toto | 3 | |
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| ucsd_kitchen_dataset_converted_externally_to_rlds | 1 | |
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| utaustin_mutex | 4 | |
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| viola | 1 | |
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### Features |
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In addition to the default features, we extracted the visual traces of future 16 frames for each frame. The dataset contains the following fields: |
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- `dataset_name`: Original source dataset name |
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- `image`: Image of the robot scene (binary) |
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- `task_string`: Description of the task |
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- `frame_index`: Index of the frame in the video |
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- `traj_index`: Index of the trajectory in the dataset |
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- `action`: Robot action vector (serialized numpy array) |
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- `trace`: Robot trajectory trace (serialized numpy array) |
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- `trace_visibility`: Visibility mask for the trace (serialized numpy array) |
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## Dataset Loading |
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### Full Dataset Load |
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```py |
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from datasets import load_dataset |
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dataset = load_dataset("MagmaAI/Magma-OXE-ToM", streaming=True, split="train") |
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``` |
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### Individual Dataset Load |
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or specify a dataset by: |
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```py |
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from datasets import load_dataset |
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dataset = load_dataset("MagmaAI/Magma-OXE-ToM", data_dir="austin_buds_dataset_converted_externally_to_rlds", streaming=True, split="train") |
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``` |
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### Sample Decoding |
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```py |
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# Helper function to deserialize binary fields |
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def deserialize_array(bytes_data): |
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return pickle.loads(bytes_data) |
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# Helper function to convert binary image data to PIL Image |
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def bytes_to_image(image_bytes): |
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return Image.open(io.BytesIO(image_bytes)) |
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for i, example in enumerate(dataset): |
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# decode the image: 256 x 256 x 3 |
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image = bytes_to_image(example['image']) |
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# decode action: 1 x 7 |
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action = deserialize_array(example['action']) |
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# decode trace: 1 x 17 x 256 x 2 |
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trace = deserialize_array(example['trace']) |
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# decode trace visibility: 1 x 17 x 256 x 1 |
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trace_visibility = deserialize_array(example['trace_visibility']) |
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``` |
