Modular Framework for Visuomotor Language Grounding

• URL: http://arxiv.org/abs/2109.02161v1
• Date: Sun, 5 Sep 2021 20:11:53 GMT
• Title: Modular Framework for Visuomotor Language Grounding
• Authors: Kolby Nottingham, Litian Liang, Daeyun Shin, Charless C. Fowlkes, Roy Fox, Sameer Singh
• Abstract summary: Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently.
• Score: 57.93906820466519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. However, data collection for these tasks is expensive and end-to-end approaches suffer from data inefficiency. We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently. Using a Language, Action, and Vision (LAV) framework removes the dependence of action and vision modules on instruction following datasets, making them more efficient to train. We also present a preliminary evaluation of LAV on the ALFRED task for visual and interactive instruction following.

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