Datasets:

EvoGym
/

robots

@@ -38,13 +38,12 @@ license: cc-by-nc-4.0
 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 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 to each other
-- reward (float): reward achieved by the robot during training
-- 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

 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