Visual Language Maps for Robot Navigation (2210.05714v4)

Abstract: Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-LLMs 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 enables natural language indexing of the map without additional labeled data. Specifically, when combined with 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. Videos are available at https://vlmaps.github.io.

Visual Language Maps for Robot Navigation: An Overview

The paper "Visual Language Maps for Robot Navigation" presents VLMaps, a novel approach that addresses the challenge of integrating visual-LLMs with spatial mapping for autonomous robot navigation. The authors introduce a method that enhances mobile robot navigation by autonomously creating spatial representations of the environment, which are enriched with visual-language features. This approach is designed to support the interpretation of natural language commands in complex spatial terms, thus improving navigation capabilities beyond mere object-centric goals.

Introduction and Motivation

Traditional robotic mapping relies heavily on geometric maps that are competent for basic path planning but often fail to meet the demands of complex spatial commands specified in natural language. Previous approaches that have attempted to bridge this gap often fall short, mainly because they separate the processes of understanding visual inputs and mapping the environment. VLMaps, on the other hand, fuse pretrained visual-language features with a 3D map, allowing robots to localize and interpret both object-centric and spatial language instructions, such as "move between the sofa and the TV."

Methodology

The methodology involves constructing a visual-language map by integrating visual features from models like LSeg into a 3D reconstruction achieved via standard robotic exploration methods. This approach permits robots to automatically harvest visual information from environments, allowing for zero-shot navigation tasks – tasks directly interpreted from natural language without requiring additional labeled data.

Key components of the method involve:

• Creating VLMaps: Using camera feeds, visual-LLMs embed pixel-level visual features that are incorporated into a 3D map of the physical space.
• Natural Language Indexing: By converting natural language descriptions into a sequence of navigation goals, robots can interpret and navigate to spatial locations in real-time.
• Embodiment-Specific Obstacle Maps: These maps are tailored to varying robot designs (e.g., drones or ground robots), accounting for their different interactions with obstacles.

Experimental Validation

The efficacy of VLMaps was tested in both simulated environments (using Habitat and AI2THOR simulators) and on physical robots like the HSR platform. The experiments show that VLMaps outperform existing methods, including CoW and LM-Nav, in scenarios that require complex navigation based on open-vocabulary and spatial instructions.

Results highlight VLMaps' superiority in tasks demanding precise spatial navigation, showcasing their ability to handle extended sequences of navigation tasks more effectively than baseline models. Notably, VLMaps excel in generalizing their navigation to new instructions without additional data, leveraging their robust integration of visual-language features with spatial map structures.

Implications and Future Developments

The implications of VLMaps are significant for both robotic autonomy and AI development. Practically, the integration with natural language processing paves the way for more intuitive human-robot interactions and flexible robotic deployment in less structured environments. The ability to construct and share obstacle maps across different robotic platforms also suggests a promising direction for multi-robot coordination tasks.

Theoretically, VLMaps' approach redefines the possibilities for visual-language grounding in real-time spatial tasks, opening avenues for further development of LLMs that are even more attuned to spatial reasoning and interaction.

In future work, improvements could focus on minimizing the sensitivity of VLMaps to 3D reconstruction noise and addressing ambiguities in object recognition within cluttered environments. Enhancements in visual-LLMs and techniques for handling dynamic scenes will be critical in maximizing the robustness and versatility of VLMaps.

In summary, the paper establishes VLMaps as a competitive, innovative step forward in the field of robotic navigation, providing substantial improvements in how robots can leverage visual-LLMs for complex, real-world tasks.