---
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
  splits:
  - name: train
    num_bytes: 62889336
    num_examples: 90563
  download_size: 6965556
  dataset_size: 62889336
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- robotics
- soft-robotics
- voxel-robot
- reinforcement learning
size_categories:
- 10K<n<100K
license: cc-by-nc-4.0
task_categories:
- robotics
---

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 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
- `reward` *(float)*: reward achieved by the robot's policy
- `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

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}
}
```