即将开课啦！具身智能目标导航算法与实战教程来了~

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, with companies like Meituan and Aethon deploying autonomous delivery robots [3]. Group 2: Technological Evolution - The evolution of Goal-Oriented Navigation can be categorized into three generations: 1. 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]. 2. Second Generation: Modular methods that explicitly construct semantic maps, breaking tasks into exploration and goal localization [5]. 3. Third Generation: Integration of large language models (LLMs) and vision-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, including natural language processing, computer vision, and reinforcement learning [9]. - A new course has been developed to address the challenges of learning Goal-Oriented Navigation, focusing on quick entry, building a research framework, and combining theory with practice [10][11][12]. Group 4: Course Structure - The course includes six chapters covering the core framework of semantic navigation, Habitat simulation ecology, end-to-end navigation methodologies, modular navigation architectures, and LLM/VLM-driven navigation systems [16][18][19][21][23]. - A significant project within the course focuses on the reproduction of VLFM algorithms and their deployment in real-world scenarios, allowing students to engage in practical applications [25].