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Modular Meta-Learning with Shrinkage., , , , , , and . NeurIPS, (2020)Learning From Demonstration in the Wild., , , , , , , , , and 1 other author(s). ICRA, page 775-781. IEEE, (2019)Human-Timescale Adaptation in an Open-Ended Task Space., , , , , , , , , and 18 other author(s). CoRR, (2023)Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ., , , , , and . AIIDE Workshops, volume 2282 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Learning from Demonstration in the Wild., , , , , , , , , and 1 other author(s). CoRR, (2018)Position: Open-Endedness is Essential for Artificial Superhuman Intelligence., , , , , , , and . ICML, OpenReview.net, (2024)On the role of planning in model-based deep reinforcement learning., , , , , , , , , and . ICLR, OpenReview.net, (2021)Haptic SLAM for context-aware robotic hand prosthetics - simultaneous inference of hand pose and object shape using particle filters., , , and . NER, page 719-722. IEEE, (2015)Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality., , , , , , and . ICLR, OpenReview.net, (2023)Many-Shot In-Context Learning., , , , , , , , , and 3 other author(s). CoRR, (2024)