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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Trajectory Prediction with Linguistic Representations Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022 BASE Show details 2 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 Abstract: Humans are remarkably flexible when under- standing new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation un- covers the limitations in the language-understanding abilities of networks. We demonstrate that these limitations can be overcome by addressing the generalization challenges in the gSCAN dataset, which explicitly measures how well an agent is able to interpret novel linguistic commands grounded in vision, e.g., novel pairings of adjectives and nouns. The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing other parameters to be learned end-to-end. We build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. Crucially, our network has the same state-of-the art performance as prior work while generalizing its knowledge when prior work does not. Our network also provides a level of interpretability that enables users to inspect what each part of networks learns. Robust grounded language understanding without dramatic failures and without corner cases is critical to building safe and fair robots; we demonstrate the significant role that compositionality can play in achieving that goal. ; This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. URL: https://hdl.handle.net/1721.1/141359 BASE Hide details 3 Trajectory Prediction with Linguistic Representations ... Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei. - : arXiv, 2021 BASE Show details 4 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Barbu, Andrei; Katz, Boris. - : Underline Science Inc., 2021 BASE Show details 5 Learning a natural-language to LTL executable semantic parser for grounded robotics Wang, Christopher; Ross, Candace; Kuo, Yen-Ling. - : Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020 BASE Show details 6 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 7 Learning a natural-language to LTL executable semantic parser for grounded robotics ... Wang, Christopher; Ross, Candace; Kuo, Yen-Ling. - : arXiv, 2020 BASE Show details 8 Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR), 2020 BASE Show details

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