Visual Language Maps for Robot Navigation

1Freiburg University, 2Google Research, 3University of Technology Nuremberg

VLMaps enables spatial goal navigation with language instructions

Abstract

Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g. image captions). While this is useful for matching images to natural language descriptions of object goals, it remains disjoint from the process of mapping the environment, so that it lacks the spatial precision of classic geometric maps. To address this problem, we propose VLMaps, a spatial map representation that directly fuses pretrained visual-language features with a 3D reconstruction of the physical world. VLMaps can be autonomously built from video feed on robots using standard exploration approaches and enable natural language indexing of the map without additional labeled data. Specifically, when combined with large language models (LLMs), VLMaps can be used to (i) translate natural language commands into a sequence of open-vocabulary navigation goals (which, beyond prior work, can be spatial by construction, e.g. "in between the sofa and TV" or "three meters to the right of the chair") directly localized in the map, and (ii) can be shared among multiple robots with different embodiments to generate new obstacle maps on-the-fly (by using a list of obstacle categories). Extensive experiments carried out in simulated and real world environments show that VLMaps enable navigation according to more complex language instructions than existing methods.

Video

Approach

VLMap Creation

The key idea behind building a VLMap is to fuse pretrained visual-language features into a geometrical reconstruction of the scene. This can be done by computing dense pixel-level embeddings from an existing visual-language model (over the RGB-D video feed of the robot) and back-projecting them onto the 3D surface of the environment (captured from depth data used for reconstruction with visual odometry). Finally, we generate the top-down scene representation by storing the visual-language features of each image pixel in the corresponding grid map pixel location.

Open-Vocabulary Landmark Indexing

We encode the open-vocabulary landmark names ("chair", "green plant", "table" etc.) with the text encoder in the Visual Language Model. Then we align the landmark names with the pixels in the VLMap by computing the cosine similarity between their embeddings. We get the mask of each landmark type with the argmax operation on the similarity score.

Navigation Policies Generation

We generate the navigation policies in the form of executable code with the help of Large Language Models. By providing a few examples in the prompt, we exploit GPT-3 to parse language instructions into a string of executable code, expressing functions or logic structures (if/else statements, for/while loops) and parameterizing API calls (e.g., robot.move_to(target_name) or robot.turn(degrees)).

Experiment

Long-Horizon Spatial Goal Navigation from Language

With open-vocabulary landmark indexing, VLMaps enables long-horizon spatial goal navigation with natural language instructions

Sequence 1

VLMaps

CLIP on Wheels

Sequence 2

VLMaps

CLIP on Wheels


Multi-Embodiment Navigation

A VLMap can be shared among different robots and enables generation of obstacle maps for different embodiments on-the-fly to improve navigation efficiency. For example, a LoCoBot (ground robot) has to avoid sofa, tables, chairs and so on during planning while a drone can ignore them. Experiments below show how a single VLMap representation in each scene can adapt to different embodiments (by generating customized obstacle maps) and improve navigation efficiency.

Move to the laptop and the box sequentially


Drone

LoCoBot

Move to the window


Drone

LoCoBot

Move to the television


Drone

LoCoBot

BibTeX

@article{huang22vlmaps,
  title     = {Visual Language Maps for Robot Navigation},
  author    = {Chenguang Huang and Oier Mees and Andy Zeng and Wolfram Burgard},
  journal   = {arXiv preprint arXiv:2210.05714},
  year      = {2022},
}