Memory-Maze: Scenario Driven Visual Language Navigation Benchmark for Guiding Blind People

Understand

Visual Language Navigation (VLN) powered robots have the potential to guide blind people by understanding route instructions provided by sighted passersby.

• This capability allows robots to operate in environments often unknown a prior.
• Existing VLN models are insufficient for the scenario of navigation guidance for blind people, as they need to understand routes described from human memory, which frequently contains stutters, errors, and omissions of details, as opposed to those obtained by thinking out loud, such as in the R2R dataset.
• However, existing benchmarks do not contain instructions obtained from human memory in natural environments.