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].

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 in various verticals, enhancing service efficiency in delivery, healthcare, and hospitality sectors [3] - The evolution of Goal-Oriented Navigation can be categorized into three generations, each with distinct methodologies and advancements [5][7] 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 instructions to autonomous decision-making involves semantic parsing, environmental modeling, and dynamic decision-making [2] - The technology has been integrated into delivery robots, service robots in healthcare and hospitality, and humanoid robots for domestic and industrial applications [3] Group 2: Technical Evolution - The first generation focuses on end-to-end methods using reinforcement and imitation learning, achieving breakthroughs in Point Navigation and closed-set image navigation tasks [5] - The second generation employs modular methods that explicitly construct semantic maps, enhancing performance in zero-shot object navigation tasks [5] - The third generation integrates large language models (LLMs) and visual language models (VLMs) to improve exploration strategies and open-vocabulary target matching accuracy [7][8] Group 3: Challenges and Learning Path - The complexity of embodied navigation requires knowledge across multiple domains, making it challenging for newcomers to grasp the necessary concepts [10] - A new course has been developed to address these challenges, focusing on practical applications and theoretical foundations of Goal-Oriented Navigation [11][12][13] - The course aims to build a comprehensive understanding of the technology stack, including end-to-end reinforcement learning, modular semantic map construction, and LLM/VLM integration methods [30]