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传统SLAM的定位导航和具身目标导航有什么区别?
具身智能之心· 2025-08-29 00:03
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 ...
具身领域的目标导航到底是什么?从目标搜索到触达有哪些路线?
具身智能之心· 2025-06-24 14:09
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]