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Learning human-robot handovers through π-STAM: Policy improvement with spatio-temporal affordance maps., , and . Humanoids, page 857-863. IEEE, (2016)A proposal for semantic map representation and evaluation., , , , , and . ECMR, page 1-6. IEEE, (2015)Reinforcement Learning for Optimization of COVID-19 Mitigation policies., , , , , , , and . CoRR, (2020)An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains., , and . J. Artif. Intell. Res., (2023)Grounding LTLf Specifications in Images., , and . NeSy, volume 3212 of CEUR Workshop Proceedings, page 45-63. CEUR-WS.org, (2022)Learning to Smooth with Bidirectional Predictive State Inference Machines., , , , and . UAI, AUAI Press, (2016)Autonomous Planetary Landing via Deep Reinforcement Learning and Transfer Learning., , and . CVPR Workshops, page 2031-2038. Computer Vision Foundation / IEEE, (2021)S-AVE: Semantic Active Vision Exploration and Mapping of Indoor Environments for Mobile Robots., , , and . AIRO@AI*IA, volume 2594 of CEUR Workshop Proceedings, page 47-52. CEUR-WS.org, (2019)Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening., , , , and . Mach. Learn., 113 (4): 2013-2044 (April 2024)Automatic Extraction of Structural Representations of Environments., , , , and . IAS, volume 302 of Advances in Intelligent Systems and Computing, page 721-733. Springer, (2014)