README.md

metadata

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. Task suite evaluations are described in our NeurIPS 2021 paper.

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. 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]
• 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

[1] This dataset is a subset of EvoGym/robots
[2] Rewards may not exactly match those in 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}
}