Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments

TL;DR

This paper introduces VLM-GroNav, a novel outdoor robot navigation method that combines vision-language models with physical terrain grounding to improve traversability assessment and navigation success.

Contribution

It presents a new approach integrating VLMs with proprioceptive data for real-time terrain understanding and dynamic path planning in outdoor environments.

Findings

Up to 50% increase in navigation success rate.

Effective handling of deformable and slippery terrains.

Validated on both legged and wheeled robots.

Abstract

We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical grounding that is used to assess intrinsic terrain properties such as deformability and slipperiness. We use proprioceptive-based sensing, which provides direct measurements of these physical properties, and enhances the overall semantic understanding of the terrains. Our formulation uses in-context learning to ground the VLM's semantic understanding with proprioceptive data to allow dynamic updates of traversability estimates based on the robot's real-time physical interactions with the environment. We use the updated traversability estimations to inform both the local and global planners for real-time trajectory replanning. We validate our method on a legged robot (Ghost Vision 60) and a wheeled robot (Clearpath Husky), in diverse real-world outdoor environments with different deformable and slippery terrains. In practice, we observe significant improvements over state-of-the-art methods by up to 50% increase in navigation success rate.