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---
dataset_info:
features:
- name: uid
dtype: string
- name: body
sequence:
sequence: int64
- name: connections
sequence:
sequence: int64
- name: reward
dtype: float64
- name: env_name
dtype: string
- name: generated_by
dtype: string
- name: policy_blob
dtype: binary
splits:
- name: train
num_bytes: 203871816
num_examples: 2553
download_size: 201084330
dataset_size: 203871816
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-4.0
task_categories:
- robotics
tags:
- robotics
- soft-robotics
- voxel-robots
- reinforcement learning
size_categories:
- 1K<n<10K
---
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).
<img src="https://github.com/EvolutionGym/evogym/raw/main/images/teaser-low-res.gif" alt="teaser" style="width: 50%; display: block; margin: auto;" />
In this dataset, we open-source 2.5k+ annotated robot structures and policies from the EvoGym paper. The fields of each robot in the dataset are as follows:
- `uid` *(str)*: Unique identifier for the robot [[1]](#note1)
- `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.
- `reward` *(float)*: reward achieved by the robot's policy [[2]](#note2)
- `env_name` *(str)*: Name of the EvoGym environment (task) the robot was trained on
- `generated_by` *("Genetic Algorithm" | "Bayesian Optimization" | "CPPN-NEAT")*: Algorithm used to generate the robot
- `policy_blob` *(binary)*: Serialized policy for the robot
<span id="note1">[1]</span> This dataset is a subset of [EvoGym/robots](https://huggingface.co/datasets/EvoGym/robots)
<span id="note2">[2]</span> Rewards may not exactly match those in [EvoGym/robots](https://huggingface.co/datasets/EvoGym/robots), due to changes in the library, system architecture, etc.
If you find this dataset helpful to your research, please cite our paper:
```
@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}
}
```
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