CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation

arXiv:2306.10322v3 Announce Type: replace
Abstract: Understanding and following natural language instructions while navigating through complex, real-world environments poses a significant challenge for general-purpose robots. These environments often include obstacles and pedestrians, making it essential for autonomous agents to possess the capability of self-corrected planning to adjust their actions based on feedback from the surroundings. However, the majority of existing vision-and-language navigation (VLN) methods primarily operate in less realistic simulator settings and do not incorporate environmental feedback into their decision-making processes. …

abstract agent agents arxiv autonomous autonomous agents capability challenge cs.ai cs.cl cs.cv environments general language making natural natural language navigation obstacles pedestrians planning robots through type understanding vision vision-and-language world zero-shot