README.md

---
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" width="800"/>)
![teaser](https://github.com/EvolutionGym/evogym/raw/main/images/teaser-low-res.gif)

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]
- 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 to each other
- reward (float): reward achieved by the robot's policy [2]
- env_name (str): name of the EvoGym environment (task) the robot was trained on
- generated_by (Literal["Genetic Algorithm", "Bayesian Optimization", "CPPN-NEAT"]): name of the algorithm that generated the robot
- policy_blob (binary): encodes the robot's policy

[1] This dataset is a subset of [EvoGym/robots](https://huggingface.co/datasets/EvoGym/robots)

[2] Rewards may not exactly match [EvoGym/robots](https://huggingface.co/datasets/EvoGym/robots), due to changes in the library, system architecture, etc.