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Refining discovered symbols with multi-step interaction experience.

, and . Humanoids, page 1007-1012. IEEE, (2015)

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High-level representations through unconstrained sensorimotor learning., , and . ICDL-EPIROB, page 1-6. IEEE, (2020)Knowledge propagation and relation learning for predicting action effects., , and . IROS, page 623-629. IEEE, (2014)Time Perception: A Review on Psychological, Computational, and Robotic Models., , and . IEEE Trans. Cogn. Dev. Syst., 14 (2): 301-315 (2022)Correspondence learning between morphologically different robots through task demonstrations., , , , and . CoRR, (2023)Special issue on symbol emergence in robotics and cognitive systems (II)., , , , , , , , , and 5 other author(s). Adv. Robotics, 36 (5-6): 217-218 (2022)Bidirectional Progressive Neural Networks with Episodic Return Progress for Emergent Task Sequencing and Robotic Skill Transfer., , , and . CoRR, (2024)Traversability: A Case Study for Learning and Perceiving Affordances in Robots., and . Adapt. Behav., 18 (3-4): 258-284 (2010)A developmental framework for learning affordances (Sağlarlık öğrenimi için gelişimsel bir çerçeve). Middle East Technical University, Turkey, (2010)Adaptive Shared Control with Human Intention Estimation for Human Agent Collaboration., , , and . CASE, page 653-658. IEEE, (2022)Deep Multi-Object Symbol Learning with Self-Attention Based Predictors., , and . SIU, page 1-4. IEEE, (2023)