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Outdoor Vision-and-Language Navigation Needs Object-Level Alignment.

Yanjun Sun1,2, Yue Qiu2, Yoshimitsu Aoki1

  • 1Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.

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|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an object-level alignment module (OAlM) for embodied AI agents. The OAlM improves outdoor vision-and-language navigation (VLN) by using landmarks as sub-goals, enhancing performance on complex datasets.

Keywords:
embodied AIlandmark-based navigationvision-and-language navigation

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Area of Science:

  • Embodied Artificial Intelligence (AI)
  • Computer Vision
  • Natural Language Processing

Background:

  • Vision-and-Language Navigation (VLN) is a complex multi-modal task in embodied AI, particularly challenging in outdoor urban environments.
  • Existing outdoor VLN models often struggle with complex environments and detailed instruction interpretation, leading to navigation failures.
  • Human navigation effectively utilizes landmarks as reference points for more efficient pathfinding.

Purpose of the Study:

  • To enhance outdoor vision-and-language navigation (VLN) performance in embodied AI.
  • To address the limitations of current models in interpreting complex environments and instructions.
  • To develop a more robust and adaptable navigation agent inspired by human landmark-based navigation.

Main Methods:

  • Proposed an object-level alignment module (OAlM) to guide agents towards recognizing and utilizing instruction-specified objects as landmarks.
  • Treated recognized landmarks as intermediate sub-goals to decompose long-range navigation paths into shorter, manageable segments.
  • Integrated the OAlM with existing panorama and instruction feature-based VLN models.

Main Results:

  • The OAlM demonstrated a more object-focused navigation strategy compared to baseline models.
  • The proposed approach significantly outperformed the baseline on the challenging outdoor VLN Touchdown dataset.
  • Achieved a 3.19% improvement in task completion (TC) rate, indicating enhanced navigation efficiency and success.

Conclusions:

  • Leveraging object-level information and landmarks as sub-goals is a promising strategy for improving embodied AI navigation.
  • The OAlM enhances agent adaptability and robustness in complex, unpredictable real-world environments.
  • This research paves the way for more advanced and efficient outdoor navigation systems in embodied AI.