Planning from Imagination: Episodic Simulation and Episodic Memory for Vision-and-Language Navigation
This addresses navigation performance for embodied agents in unseen environments, representing a novel method for a known bottleneck.
The paper tackles the problem of Vision-and-Language Navigation (VLN) in unseen environments by proposing a novel architecture with a reality-imagination hybrid memory system, achieving state-of-the-art results in Success rate weighted by Path Length (SPL).
Humans navigate unfamiliar environments using episodic simulation and episodic memory, which facilitate a deeper understanding of the complex relationships between environments and objects. Developing an imaginative memory system inspired by human mechanisms can enhance the navigation performance of embodied agents in unseen environments. However, existing Vision-and-Language Navigation (VLN) agents lack a memory mechanism of this kind. To address this, we propose a novel architecture that equips agents with a reality-imagination hybrid memory system. This system enables agents to maintain and expand their memory through both imaginative mechanisms and navigation actions. Additionally, we design tailored pre-training tasks to develop the agent's imaginative capabilities. Our agent can imagine high-fidelity RGB images for future scenes, achieving state-of-the-art result in Success rate weighted by Path Length (SPL).