FILM: Following Instructions in Language with Modular MethodsDownload PDF

29 Sept 2021, 00:32 (edited 15 Mar 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: Instruction Following, Visual Language Navigation, Embodied Instruction Following, VLN, ALFRED
  • Abstract: Recent methods for embodied instruction following are typically trained end-to-end using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46 %) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49 %). Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or low-level instructions.
  • One-sentence Summary: We propose a modular method for embodied instruction following; our method achieves SOTA on the ALFRED benchmark by a large margin while using less data by eschewing both expert trajectories and low-level instructions.
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