Core Insights - Goal-Oriented Navigation empowers robots to autonomously complete navigation tasks based on goal descriptions, marking a significant shift from traditional visual language navigation [2] - The technology has been successfully implemented across various verticals, enhancing service efficiency in delivery, healthcare, and hospitality sectors [4] - The evolution of goal-oriented navigation can be categorized into three generations, each showcasing advancements in methodologies and technologies [6][8][10] Group 1: Technology Overview - Goal-Oriented Navigation is a key aspect of embodied navigation, relying on language understanding, environmental perception, and path planning [2] - The transition from explicit instruction-based navigation to autonomous decision-making is crucial for robots to interpret and navigate complex environments [2] - The integration of computer vision, reinforcement learning, and 3D semantic understanding is essential for achieving effective navigation [2] Group 2: Industry Applications - The technology has been applied in terminal delivery scenarios, enabling robots to adapt to dynamic environments and human interactions [4] - Companies like Meituan and Starship Technologies have deployed autonomous delivery robots in urban settings, showcasing the practical application of this technology [4] - In healthcare and hospitality, companies such as Aethon and Jianneng Technology have successfully implemented service robots for autonomous delivery of medications and meals [4] Group 3: Technological Evolution - The first generation of goal-oriented navigation focused on end-to-end methods using reinforcement and imitation learning, achieving significant progress in PointNav and image navigation tasks [6] - The second generation introduced modular approaches that explicitly construct semantic maps, enhancing performance in zero-shot object navigation tasks [8] - The third generation incorporates large language models (LLMs) to improve exploration strategies and open-vocabulary target matching accuracy [10] Group 4: Learning and Development Challenges - The complexity of embodied navigation requires knowledge across multiple domains, making it challenging for newcomers to enter the field [11] - A new course has been developed to address these challenges, providing a structured learning path for mastering goal-oriented navigation technologies [11][12] - The course emphasizes practical application, helping learners transition from theoretical knowledge to real-world implementation [12][13] Group 5: Course Structure - The course is divided into several chapters, covering core frameworks, Habitat simulation, end-to-end methodologies, modular navigation architectures, and LLM/VLM-driven systems [15][17][19][21] - Practical assignments will allow students to apply their knowledge in real-world scenarios, focusing on algorithm replication and deployment [23][27] - The course aims to equip participants with the skills necessary for independent research and development in the field of goal-oriented navigation [30]

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 across 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 visual language models (VLMs) to enhance knowledge reasoning and open-vocabulary target matching [7]. Group 3: Challenges in Learning - Learning Goal-Oriented Navigation is challenging due to the need for knowledge across multiple domains, including natural language processing, computer vision, and reinforcement learning [9]. - The fragmented nature of knowledge and the abundance of literature can overwhelm beginners, making it difficult to extract frameworks and understand development trends [9]. Group 4: Course Offering - A new course has been developed to address the challenges in learning Goal-Oriented Navigation, focusing on quick entry, building a domain framework, and combining theory with practice [10][11][12]. - The course includes a comprehensive curriculum covering semantic navigation frameworks, Habitat simulation ecology, end-to-end navigation methodologies, modular navigation architectures, and LLM/VLM-driven navigation systems [13][16][17][19][20][23].

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].