具身领域的目标导航到底是什么？主流算法盘点~

Core Viewpoint - The article discusses the advancements and applications of Goal-Oriented Navigation technology, emphasizing its significance in enabling robots to autonomously navigate and make decisions in unfamiliar environments, moving from traditional instruction-based navigation to a more autonomous understanding of the world [1][2]. Group 1: Technology Overview - Goal-Oriented Navigation is a key area within embodied navigation, relying on three main technological pillars: language understanding, environmental perception, and path planning [1]. - The technology has been successfully implemented in various verticals, including delivery, healthcare, and hospitality, showcasing its ability to adapt to dynamic environments and human interactions [2]. - The evolution of Goal-Oriented Navigation can be categorized into three generations: end-to-end methods, modular approaches, and LLM/VLM integration strategies [4][6]. Group 2: Industry Applications - In delivery scenarios, Goal-Oriented Navigation combined with social navigation algorithms allows robots to perform tasks in complex urban settings, as seen with Meituan's delivery vehicles and Starship Technologies' campus robots [2]. - In healthcare and hospitality, companies like Aethon and Jianneng Technology have deployed service robots for autonomous delivery of medications and meals, enhancing service efficiency [2]. - The integration of Goal-Oriented Navigation in humanoid robots is accelerating their penetration into home services, care, and industrial logistics [2]. Group 3: Technical Progress and Challenges - The development of embodied navigation has seen significant advancements since the introduction of PointNav in 2020, with evaluation systems expanding to include ImageNav and ObjectNav [3]. - Current challenges include achieving human-level performance in open vocabulary object navigation and dynamic obstacle scenarios, despite notable progress in closed-set tasks [3]. - The introduction of frameworks like Sim2Real by Meta AI provides methodologies for transitioning from simulation training to real-world deployment [3]. Group 4: Educational Initiatives - The article highlights the creation of a comprehensive course aimed at addressing the challenges faced by newcomers in the field of Goal-Oriented Navigation, focusing on practical applications and theoretical foundations [9][10][11]. - The course structure includes a systematic approach to understanding the technology's evolution, practical training on simulation platforms, and hands-on projects to bridge theory and practice [14][15][16][18].