用物理感知增强视觉语言模型，让机器人更安全地穿越复杂户外地形。
Robot Navigation Using Physically Grounded Vision-Language Models in Outdoor Environments
- 结合本体感知数据动态校准视觉语言模型的语义理解。
- 在真实户外环境中实现最高50%的导航成功率提升。
- 适合需要应对泥地、雪地等复杂地形的移动机器人研发者。
我们提出一种新型自主机器人导航算法VLM-GroNav，用于处理户外多变的地形可通行性问题。该方法利用视觉语言模型（VLMs）并引入物理接地机制，评估地形的变形性和滑溜性等内在属性。通过本体感知传感直接获取这些物理特性，增强对地形的语义理解。采用上下文学习将本体数据与VLM的语义理解融合，实现基于机器人实时物理交互的可通行性动态更新。更新后的估计结果用于指导局部和全局规划器进行实时轨迹重规划。我们在腿式机器人（Ghost Vision 60）和轮式机器人（Clearpath Husky）上验证了该方法，在多种真实户外场景中测试了不同柔性和滑溜地形的表现。实际应用中，相较于现有最先进方法，导航成功率最高提升达50%。
原文摘要 · Abstract (English)
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.
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