diff --git a/_data/paperlist.yml b/_data/paperlist.yml
index 1c62e64..f7fdd42 100644
--- a/_data/paperlist.yml
+++ b/_data/paperlist.yml
@@ -9,7 +9,7 @@ papers:
   - Stefanos Nikolaidis
   year: 2023
   pdfurl: "https://ieeexplore.ieee.org/document/10243102/"
-  abstract: "
+  abstract: "Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks. However, finding diverse, high-performing controllers requires expensive network training and extensive tuning of a large number of hyperparameters. On the other hand, Covariance Matrix Adaptation MAP-Annealing (CMA-MAE), an evolution strategies (ES)-based quality diversity algorithm, does not have these limitations and has achieved state-of-the-art performance on standard QD benchmarks. However, CMA-MAE cannot scale to modern neural network controllers due to its quadratic complexity. We leverage efficient approximation methods in ES to propose three new CMA-MAE variants that scale to high dimensions. Our experiments show that the variants outperform ES-based baselines in benchmark robotic locomotion tasks, while being comparable with or exceeding state-of-the-art deep reinforcement learning-based quality diversity algorithms."
   bibtex: |
           "@ARTICLE{10243102,
   author={Tjanaka, Bryon and Fontaine, Matthew C. and Lee, David H. and Kalkar, Aniruddha and Nikolaidis, Stefanos},