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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following., , , and . ICLR (Poster), OpenReview.net, (2019)Crisp sets as classes of discontinuous fuzzy sets., , , and . Int. J. Approx. Reason., 50 (8): 1298-1305 (2009)What Computing with Words Means to Me Discussion Forum., , , , , , and . IEEE Comput. Intell. Mag., 5 (1): 20-26 (2010)PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations., , , , , and . IROS, page 1447-1454. IEEE, (2022)Combining Crisp and Fuzzy Logic in a Prolog Compiler., , and . APPIA-GULP-PRODE, page 23-38. Facultad de Informática, Universidad Politécnica de Madrid, (2002)Robust Gesture Recognition using a Prediction-Error-Classification Approach., and . FUZZ-IEEE, page 1-7. IEEE, (2007)Measuring the Reliability of Reinforcement Learning Algorithms., , , , and . CoRR, (2019)Cross-modal adaptation for RGB-D detection., , , , and . ICRA, page 5032-5039. IEEE, (2016)Predictive Information Accelerates Learning in RL., , , , , , and . NeurIPS, (2020)The Devil is in the Decoder: Classification, Regression and GANs., , , , , , and . Int. J. Comput. Vis., 127 (11-12): 1694-1706 (2019)