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    Evolving graphical planner: Contextual global planning for vision-and-language navigation

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    Type
    Conference Paper
    Authors
    Deng, Zhiwei
    Narasimhan, Karthik
    Russakovsky, Olga
    KAUST Grant Number
    OSRCRG2017-3405
    Date
    2020-01-01
    Permanent link to this record
    http://hdl.handle.net/10754/664432
    
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    Abstract
    The ability to perform effective planning is crucial for building an instruction-following agent. When navigating through a new environment, an agent is challenged with (1) connecting the natural language instructions with its progressively growing knowledge of the world; and (2) performing long-range planning and decision making in the form of effective exploration and error correction. Current methods are still limited on both fronts despite extensive efforts. In this paper, we introduce the Evolving Graphical Planner (EGP), a model that performs global planning for navigation based on raw sensory input. The model dynamically constructs a graphical representation, generalizes the action space to allow for more flexible decision making, and performs efficient planning on a proxy graph representation. We evaluate our model on a challenging Vision-and-Language Navigation (VLN) task with photorealistic images, and achieve superior performance compared to previous navigation architectures. For instance, we achieve a 53% success rate on the test split of the Room-to-Room navigation task [1] through pure imitation learning, outperforming previous navigation architectures by up to 5%.
    Sponsors
    This work is partially supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSRCRG2017-3405 and by Princeton University’s Center for Statistics and Machine Learning (CSML) DataX fund. We would also like to thank Felix Yu, Angelina Wang and Zeyu Wang for offering insightful discussions and comments on the paper.
    Publisher
    Neural information processing systems foundation
    Conference/Event name
    34th Conference on Neural Information Processing Systems, NeurIPS 2020
    arXiv
    2007.05655
    Additional Links
    https://proceedings.neurips.cc/paper/2020/hash/eddb904a6db773755d2857aacadb1cb0-Abstract.html
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