IcarusWizard/AIME-NoB

Data Card

Motivation

For what purpose was the dataset created?

The dataset is created for the experiment section of our paper "Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models" to pretrained world models and do imitation learning. We release the datasets for the community as a common test bench for similar problems. Code available at https://github.com/IcarusWizard/AIME-NoB.

Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?

The dataset is collected by Xingyuan Zhang during his Ph.D. at Machine Learning Research Lab at Volkswagen AG.

Uses

Has the dataset been used for any tasks already?

Yes, the datasets has been used in our AIME-NoB paper for pretraining world models and imitation learning from observation.

Is there a repository that links to any or all papers or systems that use the dataset?

No.

What (other) tasks could the dataset be used for?

The datasets can also be used for offline reinforcement learning.

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?

No. Everything from the simulator is recorded in the dataset.

Are there tasks for which the dataset should not be used?

Not at the moment.

Data description

What data does each instance consist of? What is the format of it?

Every dataset consists of certain number of trajectories and each trajecory is stored as a separate .hdf5 file. The .hdf5 file can be loaded by h5py.File which give you a dictionary-like structure with each entry as a np.ndarray. The dictionary has both the proprioceptions and the images for each time step. Note: the key pre_action means the actions taken by the agent one time step before which leads to the current observation, hence all the pre_action in the first time step is 0.

Are there recommended data splits (e.g., training, development/validation, testing)?

Each dataset is self-contained, we don't have a recommended data splits inside of it.

Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?

Yes, the datasets are self-contained.

Is there any example code for loading the dataset?

import os
from aime_nob.data import SequenceDataset
from aime_nob.utils import DATA_PATH

dataset_name = 'walker-plan2explore-buffer'
dataset = SequenceDataset(os.path.join(DATA_PATH, dataset_name), horizon=50, overlap=True)

Data Creation

The buffer datasets for DMC are collected by running plan2explore algorithm on each environment with the visual setup for 2000 trajectories and taking the replay buffer. The result dataset has 2005 trajectories in total due to the initial warmup with 5 random trajectories. For example, you can collect the walker-plan2explore-buffer dataset by python train_scripts/train_plan2explore.py env=walker environment_setup=visual.

The MetaWorld expert datasets are collected by the trained policies from tdmpc2 for 50 trajectories.

Distribution

How will the dataset will be distributed (e.g., tarball on website, API, GitHub)?

The datasets will be hosted with HuggingFace.

When will the dataset be distributed?

May 2024.

Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)?

CC BY 4.0.

Have any third parties imposed IP-based or other restrictions on the data associated with the instances?

No.

Maintenance

Who will be supporting/hosting/maintaining the dataset?

Xingyuan Zhang will maintain this dataset. You can contact him with [email protected].

Will there be an erratum? If yes, how should people get access to that?

There won't.

Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)?

Not planned, but may act as requested from the community.

Will older versions of the dataset continue to be supported/hosted/maintained?

Yes.

If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?

The dataset is free to use, people can build their own work on it and release by themselves.

Additional Information

Version

Version 1.0, the initial release.

Dataset Curators

The dataset is collected by Xingyuan Zhang during his Ph.D. at Machine Learning Research Lab at Volkswagen AG.

Licensing Information

© 2024. This work is licensed under a CC BY 4.0 license.

Citation Information

If you find the datasets useful, please cite our paper.

@misc{zhang2024overcoming,
    title={Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models}, 
    author={Xingyuan Zhang and Philip Becker-Ehmck and Patrick van der Smagt and Maximilian Karl},
    year={2024},
    eprint={2404.18896},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}