Open X-Embodiment aims to provide all open-sourced robotic data in the same unified format, for easy downstream consumption.
The first publication using the Open X-Embodiment dataset is Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Each data set is represented as a sequence of episodes, where each episode is represented using the RLDS episode format.
We provide a self-contained colab that demonstrates how to visualize a few episodes from each data set, and how to create batches of data ready for training and inference.
We provide the list of dataset that is included as part of the open-sourcing effort and their metadata in the dataset spreadsheet.
The model takes as input a RGB image from the robot workspace camera and a task string describing the task that the robot is supposed to perform.
What task the model should perform is communicated to the model purely through the task string. The image communicates to the model the current state of the world, i.e. assuming the model runs at three hertz, every 333 milliseconds, we feed the latest RGB image from a robot workspace camera into the model to obtain the next action to take.
Please note that the model currently does not take in additional camera images such as wrist camera images, in hand camera images, or depth.
The action dimensions we consider include seven variables for the gripper movement (x, y, z, roll, pitch, yaw, opening of the gripper). Each variable represents the absolute value, the delta change to the dimension value or the velocity of the dimension.
The inference colab of trained RT-1-X Tensorflow checkpoint demonstrates how to load the model checkpoint, run inference on offline episodes and overlay the predicted and ground truth action.
A jax checkpoint that can be used by the flax checkpoint loader in the rt1_inference_example.py can be downloaded by
gsutil -m cp -r gs://gdm-robotics-open-x-embodiment/open_x_embodiment_and_rt_x_oss/rt_1_x_jax .
If you run into this issue when trying to run tfds.load({dataset_name})
tensorflow_datasets.core.registered.DatasetNotFoundError: Dataset {dataset_name} not found.
Try downloading the dataset manually by running
gsutil -m cp -r gs://gdm-robotics-open-x-embodiment/{dataset_name} ~/tensorflow_datasets/
Once you download the dataset like this, you can use the dataset with the regular tfds.load({dataset_name}) command!
If you're using the Open X-Embodiment dataset and RT-X in your research, please cite. If you're specifically using datasets that have been contributed to the joint effort, please cite those as well. The dataset spreadsheet contains the citation for each dataset for your convenience.
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Copyright 2023 DeepMind Technologies Limited.
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