|
|
--- |
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|
dataset_info: |
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features: |
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|
- name: uid |
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dtype: string |
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|
- name: body |
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sequence: |
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|
sequence: int64 |
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|
- name: connections |
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sequence: |
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sequence: int64 |
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|
- name: reward |
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dtype: float64 |
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|
- name: env_name |
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|
dtype: string |
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|
- name: generated_by |
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|
dtype: string |
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splits: |
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|
- name: train |
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|
num_bytes: 62889336 |
|
|
num_examples: 90563 |
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|
download_size: 6965556 |
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dataset_size: 62889336 |
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|
configs: |
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|
- config_name: default |
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|
data_files: |
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- split: train |
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path: data/train-* |
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tags: |
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|
- robotics |
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|
- soft-robotics |
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|
- voxel-robot |
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- reinforcement learning |
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size_categories: |
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- 10K<n<100K |
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license: cc-by-nc-4.0 |
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task_categories: |
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|
- robotics |
|
|
--- |
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|
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|
Evolution Gym is a large-scale benchmark for co-optimizing the design and control of soft robots. It provides a lightweight soft-body simulator wrapped with a gym-like interface for developing learning algorithms. EvoGym also includes a suite of 32 locomotion and manipulation tasks, detailed on our [website](https://evolutiongym.github.io/all-tasks). Task suite evaluations are described in our [NeurIPS 2021 paper](https://arxiv.org/pdf/2201.09863). |
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|
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|
<img src="https://github.com/EvolutionGym/evogym/raw/main/images/teaser-low-res.gif" alt="teaser" style="width: 50%; display: block; margin: auto;" /> |
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|
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|
In this dataset, we open-source 90k+ annotated robot structures from the EvoGym paper. The fields of each robot in the dataset are as follows: |
|
|
- `uid` *(str)*: Unique identifier for the robot |
|
|
- `body` *(int64 np.ndarray)*: 2D array indicating the voxels that make up the robot |
|
|
- `connections` *(int64 np.ndarray)*: 2D array indicating how the robot's voxels are connected. In this dataset, all robots are fully-connected, meaning that all adjacent voxels are connected |
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|
- `reward` *(float)*: reward achieved by the robot's policy |
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|
- `env_name` *(str)*: Name of the EvoGym environment (task) the robot was trained on |
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|
- `generated_by` *("Genetic Algorithm" | "Bayesian Optimization" | "CPPN-NEAT")*: Algorithm used to generate the robot |
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|
|
|
|
If you find this dataset helpful to your research, please cite our paper: |
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|
|
|
|
``` |
|
|
@article{bhatia2021evolution, |
|
|
title={Evolution gym: A large-scale benchmark for evolving soft robots}, |
|
|
author={Bhatia, Jagdeep and Jackson, Holly and Tian, Yunsheng and Xu, Jie and Matusik, Wojciech}, |
|
|
journal={Advances in Neural Information Processing Systems}, |
|
|
volume={34}, |
|
|
year={2021} |
|
|
} |
|
|
``` |
