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Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments
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Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments
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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.
Forward citations
Cited by 5 Pith papers
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PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation
PathPainter transfers image generation models to embodied navigation by generating traversability masks from BEV images and language instructions while using cross-view localization to reduce odometry drift.
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A vision-language model drives a simulated greenhouse robot through simple crop-inspection tasks with ~87% success, but long multi-target tasks collapse to under 10% success.
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Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation
Splatblox creates a traversability-aware ESDF from RGB-LiDAR fusion via Gaussian Splatting, enabling semantic navigation that outperforms prior methods by over 50% success rate in vegetated field trials on quadruped a...
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A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation
VLN methods show a large sim-to-real gap; a hierarchical system reaches 51% real-world success versus 22% for a monolithic RGB-only system across ten physical scenes.
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PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation
Navigation system transfers image generation models to embodied tasks via BEV-based traversability mask generation from language and cross-view localization for odometry correction, shown on a UAV completing 160m outd...
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