By creating instance-aware semantic maps, IVLMap makes it possible for robots to precisely follow navigation instructions in plain language.

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The Instance-aware Visual Language Map (IVLMap) framework for natural language-based robot navigation is implemented in this part. By creating a semantic map that encodes instance-level and attribute-level data, IVLMap enables robots to recognize spatial relationships and differentiate between several similar items (such as the "third black chair"). In order to read linguistic commands, break them down into structured subgoals, and produce executable robot navigation code, the suggested system incorporates Large Language Models (LLMs), such as ChatGPT and Llama 2.