Audio Visual Language Maps for Robot Navigation

While interacting with the world is a multi-sensory experience, many robots continue to predominantly rely on visual perception to map and navigate in their environments. We propose AVLMaps, a 3D spatial map representation that stores cross-modal information from audio, visual, and language cues. AVLMaps fuse features from pretrained multimodal foundation models into a multi-layer representation. This enables robots to index goals in the map based on multimodal queries, such as textual descriptions, images, or audio snippets of landmarks. AVLMaps allow for zero-shot multimodal spatial goal navigation and perform better than alternatives in ambiguous scenarios. These capabilities extend to mobile robots in the real world. Videos and code are available at https://avlmaps.github.io.