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Filters: Author is Yen-Ling Kuo [Clear All Filters]
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).
Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).
Learning a natural-language to LTL executable semantic parser for grounded robotics. (2020). doi:https://doi.org/10.48550/arXiv.2008.03277
Learning a Natural-language to LTL Executable Semantic Parser for Grounded Robotics. (Proceedings of Conference on Robot Learning (CoRL-2020), 2020). at <https://corlconf.github.io/paper_385/>
Deep Compositional Robotic Planners that Follow Natural Language Commands. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), (2019). at <https://vigilworkshop.github.io/>
Deep sequential models for sampling-based planning. The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) (2018). doi:10.1109/IROS.2018.8593947