Update README.md

README.md

@@ -33,4 +33,18 @@ tags:
 - reinforcement learning
 size_categories:
 - 10K<n<100K
----
+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