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 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
[2] Rewards may not exactly match EvoGym/robots, due to changes in the library, system architecture, etc.