Core Viewpoint - Goal-Oriented Navigation empowers robots to autonomously complete navigation tasks based on goal descriptions, marking a significant shift from traditional visual language navigation systems [2][3]. Group 1: Technology Overview - Embodied navigation is a core area of embodied intelligence, relying on three technical pillars: language understanding, environmental perception, and path planning [2]. - Goal-Oriented Navigation requires robots to explore and plan paths in unfamiliar 3D environments using only goal descriptions such as coordinates, images, or natural language [2]. - The technology has been industrialized in various verticals, including delivery, healthcare, and hospitality, enhancing service efficiency [3]. Group 2: Technological Evolution - The evolution of Goal-Oriented Navigation can be categorized into three generations: - First Generation: End-to-end methods focusing on reinforcement learning and imitation learning, achieving breakthroughs in Point Navigation and closed-set image navigation tasks [5]. - Second Generation: Modular methods that explicitly construct semantic maps, breaking tasks into exploration and goal localization [5]. - Third Generation: Integration of large language models (LLMs) and visual language models (VLMs) to enhance knowledge reasoning and open vocabulary target matching [7]. Group 3: Challenges and Learning Path - The complexity of embodied navigation, particularly Goal-Oriented Navigation, necessitates knowledge from multiple fields, making it challenging for newcomers to enter the domain [9]. - A new course has been developed to address these challenges, focusing on quick entry, building a research framework, and combining theory with practice [10][11][12]. Group 4: Course Structure - The course will cover the theoretical foundations and technical lineage of Goal-Oriented Navigation, including task definitions and evaluation benchmarks [15]. - It will also delve into the Habitat simulation ecosystem, end-to-end navigation methodologies, modular navigation architectures, and LLM/VLM-driven navigation systems [16][18][20][22]. - A significant project will focus on the reproduction of VLFM algorithms and their deployment in real-world scenarios [24].

目标驱动导航，赋予机器人自主完成导航目标 具身导航作为具身智能的核心领域，涉及语言理解、环境感知、路径规划三大技术支柱。目标驱动导航（Goal-Oriented Navigation）通过赋予机器人自主决策能 力，是具身导航中最具代表性的方向。 目标驱动导航要求智能体在陌生的三维环境中，仅凭目标描述（如坐标、图片、自然语言）等，即可自主完成环境探索与 路径规划。 与传统视觉语言导航（VLN）依赖显式指令不同，目标驱动导航系统需要实现从"听懂指令走对路"到"看懂世界自己找路"的跃迁：当人类下达"去厨房拿可乐"的指 令时，机器人需自主完成语义解析（识别厨房空间特征与可乐视觉属性）、环境建模（构建家居场景的空间拓扑）以及动态决策（避开移动的人类或宠物），这 背后凝聚着计算机视觉、强化学习与3D语义理解的交叉突破。 目标驱动导航技术已在多个垂直领域实现产业化落地。在终端配送场景中，该技术与社交导航算法结合，使机器人具备应对动态环境和人际交互的能力：美团无 人配送车通过动态路径重规划在复杂城市环境中执行递送任务，Starship Technologies的园区配送机器人已在欧美高校和社区部署。在医疗、酒店及餐饮场景，嘉 ...