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Trajectory Prediction with Linguistic Representations
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Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei; McGill, Stephen G.; Katz, Boris; Leonard, John J.; Rosman, Guy. - : Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022
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Abstract:
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory sam- ples with partially-annotated captions. The model learns the meaning of each of the words without direct per-word supervision. At inference time, it generates a linguistic description of trajectories which captures maneuvers and interactions over an extended time interval. This generated description is used to refine predictions of the trajectories of multiple agents. We train and validate our model on the Argoverse dataset, and demonstrate improved accuracy results in trajectory prediction. In addition, our model is more interpretable: it presents part of its reasoning in plain language as captions, which can aid model development and can aid in building confidence in the model before deploying it. ; This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216.
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URL: https://hdl.handle.net/1721.1/141362
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
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Measuring Social Biases in Grounded Vision and Language Embeddings ...
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
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Assessing Language Proficiency from Eye Movements in Reading
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In: Association for Computational Linguistics (2021)
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Measuring Social Biases in Grounded Vision and Language Embeddings
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Ross, Candace; Barbu, Andrei; Katz, Boris. - : Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021
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Learning a natural-language to LTL executable semantic parser for grounded robotics
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Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ...
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Learning a natural-language to LTL executable semantic parser for grounded robotics ...
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Measuring Social Biases in Grounded Vision and Language Embeddings ...
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Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
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