Magma: A Foundation Model for Multimodal AI Agents
Paper • 2502.13130 • Published • 58
dataset_name stringclasses 2
values | image unknown | task_string stringclasses 5
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End of preview. Expand in Data Studio
Magma: A Foundation Model for Multimodal AI Agents
Jianwei Yang*1† Reuben Tan1† Qianhui Wu1† Ruijie Zheng2‡ Baolin Peng1‡ Yongyuan Liang2‡
Yu Gu1 Mu Cai3 Seonghyeon Ye4 Joel Jang5 Yuquan Deng5 Lars Liden1 Jianfeng Gao1▽
1 Microsoft Research; 2 University of Maryland; 3 University of Wisconsin-Madison
4 KAIST; 5 University of Washington
* Project lead † First authors ‡ Second authors ▽ Leadership
[arXiv Paper] [Project Page] [Hugging Face Paper] [Github Repo] [Video]
Introduction
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".
The dataset is organized by following source datasets, with each source containing one or more arrow files:
| Folder | Number of Shards |
|---|---|
| austin_buds_dataset_converted_externally_to_rlds | 1 |
| austin_sailor_dataset_converted_externally_to_rlds | 4 |
| austin_sirius_dataset_converted_externally_to_rlds | 3 |
| berkeley_autolab_ur5 | 1 |
| berkeley_cable_routing | 1 |
| berkeley_fanuc_manipulation | 1 |
| bridge_orig | 17 |
| cmu_stretch | 1 |
| dlr_edan_shared_control_converted_externally_to_rlds | 1 |
| fractal20220817_data | 21 |
| furniture_bench_dataset_converted_externally_to_rlds | 4 |
| iamlab_cmu_pickup_insert_converted_externally_to_rlds | 2 |
| jaco_play | 1 |
| kuka | 21 |
| language_table | 8 |
| nyu_franka_play_dataset_converted_externally_to_rlds | 1 |
| roboturk | 3 |
| stanford_hydra_dataset_converted_externally_to_rlds | 4 |
| taco_play | 3 |
| toto | 3 |
| ucsd_kitchen_dataset_converted_externally_to_rlds | 1 |
| utaustin_mutex | 4 |
| viola | 1 |
Features
In addition to the default features, we extracted the visual traces of future 16 frames for each frame. The dataset contains the following fields:
dataset_name: Original source dataset nameimage: Image of the robot scene (binary)task_string: Description of the taskframe_index: Index of the frame in the videotraj_index: Index of the trajectory in the datasetaction: Robot action vector (serialized numpy array)trace: Robot trajectory trace (serialized numpy array)trace_visibility: Visibility mask for the trace (serialized numpy array)
Dataset Loading
Full Dataset Load
from datasets import load_dataset
dataset = load_dataset("MagmaAI/Magma-OXE-ToM", streaming=True, split="train")
Individual Dataset Load
or specify a dataset by:
from datasets import load_dataset
dataset = load_dataset("MagmaAI/Magma-OXE-ToM", data_dir="austin_buds_dataset_converted_externally_to_rlds", streaming=True, split="train")
Sample Decoding
# Helper function to deserialize binary fields
def deserialize_array(bytes_data):
return pickle.loads(bytes_data)
# Helper function to convert binary image data to PIL Image
def bytes_to_image(image_bytes):
return Image.open(io.BytesIO(image_bytes))
for i, example in enumerate(dataset):
# decode the image: 256 x 256 x 3
image = bytes_to_image(example['image'])
# decode action: 1 x 7
action = deserialize_array(example['action'])
# decode trace: 1 x 17 x 256 x 2
trace = deserialize_array(example['trace'])
# decode trace visibility: 1 x 17 x 256 x 1
trace_visibility = deserialize_array(example['trace_visibility'])
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