Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
Paper • 2201.09863 • Published
uid stringlengths 36 36 | body sequencelengths 5 5 | connections sequencelengths 2 2 | reward float64 -0.81 63 | env_name stringclasses 32
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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 robotconnections(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 ongenerated_by("Genetic Algorithm" | "Bayesian Optimization" | "CPPN-NEAT"): Algorithm used to generate the robotpolicy_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}
}
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