Update README.md

README.md

@@ -34,6 +34,8 @@ tags:
 size_categories:
 - 10K<n<100K
 license: cc-by-nc-4.0
+task_categories:
+- robotics
 ---
 
 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).
@@ -46,4 +48,16 @@ In this dataset, we open-source 90k+ annotated robot structures from the EvoGym
 - `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
+- `generated_by` *("Genetic Algorithm" | "Bayesian Optimization" | "CPPN-NEAT")*: Algorithm used to generate the robot
+
+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}
+}
+```