Stubborn: A Strong Baseline for Indoor Object Navigation

• URL: http://arxiv.org/abs/2203.07359v1
• Date: Mon, 14 Mar 2022 17:55:00 GMT
• Title: Stubborn: A Strong Baseline for Indoor Object Navigation
• Authors: Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal
• Abstract summary: We present a strong baseline that surpasses the performance of previously published methods on the Habitat Challenge task. Our method is motivated from primary failure modes of prior state-of-the-art: poor exploration, inaccurate object identification, and agent getting trapped due to imprecise map construction.
• Score: 11.947727956369874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a strong baseline that surpasses the performance of previously published methods on the Habitat Challenge task of navigating to a target object in indoor environments. Our method is motivated from primary failure modes of prior state-of-the-art: poor exploration, inaccurate object identification, and agent getting trapped due to imprecise map construction. We make three contributions to mitigate these issues: (i) First, we show that existing map-based methods fail to effectively use semantic clues for exploration. We present a semantic-agnostic exploration strategy (called Stubborn) without any learning that surprisingly outperforms prior work. (ii) We propose a strategy for integrating temporal information to improve object identification. (iii) Lastly, due to inaccurate depth observation the agent often gets trapped in small regions. We develop a multi-scale collision map for obstacle identification that mitigates this issue.

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