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When humans conceive how to perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks.
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Using classical planners for tasks with continuous operators in robotics
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Adam: A method for stochastic optimization
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Modular multitask reinforcement learning with policy sketches
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Neural Modular Control for Embodied Question Answering
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Hierarchical decision making by generating and following natural language instructions
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Learning programmatic idioms for scalable semantic parsing
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Language as an abstraction for hierarchical deep reinforcement learning
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Synthesizing environment-aware activities via activity sketches
Yuan-Hong Liao, Xavier Puig, Marko Boben, Antonio Torralba, and Sanja Fidler. 2019 · 2019
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Program synthesis and semantic parsing with learned code idioms
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Executing instructions in situated collaborative interactions
Alane Suhr, Claudia Yan, Jack Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, and Yoav Artzi. 2019 · 2019
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Regression planning networks
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Multi-target embodied question answering
Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L. Berg, and Dhruv Batra. 2019 · 2019
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Compositional generalization via neural-symbolic stack machines
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Robothor: An open simulation-to-real embodied AI platform
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Mapping natural language instructions to mobile UI action sequences
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ALFRED: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. 2020a · 2020
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Program guided agent
Shao-Hua Sun, Te-Lin Wu, and Joseph J. Lim. 2020 · 2020
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Vision-language navigation with self-supervised auxiliary reasoning tasks
Fengda Zhu, Yi Zhu, Xiaojun Chang, and Xiaodan Liang. 2020 · 2020
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Grounding open-domain instructions to automate web support tasks
Nancy Xu, Sam Masling, Michael Du, Giovanni Campagna, Larry Heck, James Landay, and Monica Lam. 2021 · 2021
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