Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation

Vision-Language Navigation is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. We propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL) and further introduce a Self-Supervised Imitation Learning (SIL) method to explore unseen environments by imitating its own past, good decisions.

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