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Vision-Language Navigation Policy Learning and Adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 15, 2020
Summary
This study introduces Reinforced Cross-Modal Matching (RCM) and Self-Supervised Imitation Learning (SIL) to improve vision-language navigation (VLN) agents. These methods enhance instruction following and generalization in unseen environments.
Area of Science:
- Artificial Intelligence
- Robotics
- Computer Vision
Background:
- Vision-language navigation (VLN) enables embodied agents to follow natural language instructions in 3D environments.
- Key challenges include cross-modal grounding, handling ambiguous feedback, and generalizing to new environments.
Purpose of the Study:
- To develop novel methods for addressing critical challenges in vision-language navigation.
- To improve the agent's ability to ground language instructions to visual scenes and generalize to unseen environments.
Main Methods:
- Proposed Reinforced Cross-Modal Matching (RCM) using reinforcement learning for local and global grounding.
- Introduced Self-Supervised Imitation Learning (SIL) to enhance policy generalization in unseen environments.
Main Results:
- RCM achieved state-of-the-art performance, improving Success Rate weighted by Path Length (SPL) by 10% over baselines.
- SIL significantly reduced the performance gap between seen and unseen environments from 30.7% to 11.7%.
Conclusions:
- The proposed RCM and SIL methods effectively address core challenges in VLN.
- These advancements lead to more robust and generalizable embodied agents capable of complex navigation tasks.
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