MagmaAI/Magma-OXE-ToM

Magma: A Foundation Model for Multimodal AI Agents

Jianwei Yang^*1†  Reuben Tan^1†  Qianhui Wu^1†  Ruijie Zheng^2‡  Baolin Peng^1‡  Yongyuan Liang^2‡

Yu Gu¹  Mu Cai³  Seonghyeon Ye⁴  Joel Jang⁵  Yuquan Deng⁵  Lars Liden¹  Jianfeng Gao^1▽

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 name
• image: Image of the robot scene (binary)
• task_string: Description of the task
• frame_index: Index of the frame in the video
• traj_index: Index of the trajectory in the dataset
• action: 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'])