HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation

• URL: http://arxiv.org/abs/2409.14296v1
• Date: Sun, 22 Sep 2024 02:12:29 GMT
• Title: HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation
• Authors: Naoki Yokoyama, Ram Ramrakhya, Abhishek Das, Dhruv Batra, Sehoon Ha,
• Abstract summary: We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON) HM3D-OVON incorporates over 15k annotated instances of household objects across 379 distinct categories. We find that HM3D-OVON can be used to train an open-vocabulary ObjectNav agent that achieves both higher performance and is more robust to localization and actuation noise than the state-of-the-art ObjectNav approach.
• Score: 39.54854283833085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the Habitat-Matterport 3D Open Vocabulary Object Goal Navigation dataset (HM3D-OVON), a large-scale benchmark that broadens the scope and semantic range of prior Object Goal Navigation (ObjectNav) benchmarks. Leveraging the HM3DSem dataset, HM3D-OVON incorporates over 15k annotated instances of household objects across 379 distinct categories, derived from photo-realistic 3D scans of real-world environments. In contrast to earlier ObjectNav datasets, which limit goal objects to a predefined set of 6-20 categories, HM3D-OVON facilitates the training and evaluation of models with an open-set of goals defined through free-form language at test-time. Through this open-vocabulary formulation, HM3D-OVON encourages progress towards learning visuo-semantic navigation behaviors that are capable of searching for any object specified by text in an open-vocabulary manner. Additionally, we systematically evaluate and compare several different types of approaches on HM3D-OVON. We find that HM3D-OVON can be used to train an open-vocabulary ObjectNav agent that achieves both higher performance and is more robust to localization and actuation noise than the state-of-the-art ObjectNav approach. We hope that our benchmark and baseline results will drive interest in developing embodied agents that can navigate real-world spaces to find household objects specified through free-form language, taking a step towards more flexible and human-like semantic visual navigation. Code and videos available at: naoki.io/ovon.

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