Update README.md

README.md

@@ -28,4 +28,32 @@ configs:
   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](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 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](https://huggingface.co/datasets/EvoGym/robots)
+
+[2] Rewards may not exactly match [EvoGym/robots](https://huggingface.co/datasets/EvoGym/robots), due to changes in the library, system architecture, etc.