传统导航和具身目标导航到底有啥区别？

Core Viewpoint - The article discusses the evolution of robot navigation technology from traditional mapping and localization to large model-based navigation, which includes visual language navigation (VLN) and goal navigation. VLN focuses on following instructions, while goal navigation emphasizes understanding the environment to find paths independently [1][4]. Group 1: Visual Language Navigation (VLN) - VLN is fundamentally a task of following instructions, which involves understanding language commands, perceiving the environment, and planning movement strategies. The VLN robot system consists of a visual language encoder, environmental history representation, and action strategy modules [2]. - The key challenge in VLN is how to effectively compress information from visual and language inputs, with current trends favoring the use of large-scale pre-trained visual language models and LLMs for instruction breakdown and task segmentation [2][3]. - The learning of the strategy network has shifted from extracting patterns from labeled datasets to distilling effective planning information from LLMs, which has become a recent research focus [3]. Group 2: Goal Navigation - Goal navigation extends VLN by requiring agents to autonomously explore and plan paths in unfamiliar 3D environments based solely on target descriptions, such as coordinates or images [4]. - Unlike traditional VLN that relies on explicit instructions, goal-driven navigation systems must transition from "understanding commands to finding paths" by autonomously parsing semantics, modeling environments, and making dynamic decisions [6]. Group 3: Commercial Applications and Demand - Goal-driven navigation technology has been industrialized in various verticals, such as terminal delivery, where it combines with social navigation algorithms to handle dynamic environments and human interactions. Examples include Meituan's delivery robots and Starship Technologies' campus delivery robots [8]. - In sectors like healthcare, hospitality, and food service, companies like 嘉楠科技, 云迹科技, and Aethon have deployed service robots for autonomous delivery, enhancing service response efficiency [8]. - The development of humanoid robots has led to an increased focus on the adaptability of navigation technology, with companies like Unitree and Tesla showcasing advanced navigation capabilities [9]. Group 4: Knowledge and Learning Challenges - Both VLN and goal navigation require knowledge across multiple domains, including natural language processing, computer vision, reinforcement learning, and graph neural networks, making it a challenging learning path for newcomers [10].