G.Paolo, "Learning in Sparse Rewards settings through Quality-Diversity algorithms", ArXiv link, PhD Thesis
G. Paolo, A. Coninx, A. Laflaquière, S. Doncieux, "Discovering and Exploiting Sparse Rewards in a Learned Behavior Space", Under Review at ECJ MIT, ArXiv link
G. Paolo, A. Coninx, S. Doncieux, A. Laflaquière, "Sparse Reward Exploration via Novelty Search and Emitters", The Genetic and Evolutionary Computation Conference 2021, ArXiv link. Nominated for best paper award in the CS track.
G. Paolo, A. Laflaquière, A. Coninx, S. Doncieux, "Unsupervised Learning and Exploration of Reachable Outcome Space", 2020 IEEE International Conference on Robotics and Automation (ICRA), ArXiv link
S. Doncieux, G. Paolo, A. Laflaquière, A. Coninx, "Novelty Search makes Evolvability Inevitable", 2020 Genetic and Evolutionary Computation Conference (GECCO), ArXiv link
M. Pfeiffer, G. Paolo, H. Sommer, J. Nieto, R. Siegwart, C. Cadena, "A data-driven model for interaction-aware pedestrian motion prediction in object cluttered environments", 2018 IEEE International Conference on Robotics and Automation (ICRA), ArXiv link
L. Tai, G. Paolo, M. Liu, "Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation", 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , ArXiv link