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Incremental decision making in real-world environments is one of the mostchallenging tasks in embodied artificial intelligence. One particularlydemanding scenario is Vision and Language Navigation~(VLN) which requiresvisual and natural language understanding as well as spatial and temporalreasoning capabilities. The embodied agent needs to ground its understanding ofnavigation instructions in observations of a real-world environment like StreetView. Despite the impressive results of LLMs in other research areas, it is anongoing problem of how to best connect them with an interactive visualenvironment. In this work, we propose VELMA, an embodied LLM agent that uses averbalization of the trajectory and of visual environment observations ascontextual prompt for the next action. Visual information is verbalized by apipeline that extracts landmarks from the human written navigation instructionsand uses CLIP to determine their visibility in the current panorama view. Weshow that VELMA is able to successfully follow navigation instructions inStreet View with only two in-context examples. We further finetune the LLMagent on a few thousand examples and achieve 25%-30% relative improvement intask completion over the previous state-of-the-art for two datasets.

VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View