港科大等提出LOVON：足式机器人开放世界全域目标追踪新范式！

Core Viewpoint - The article introduces the LOVON framework, which integrates large language models, open vocabulary visual detection, and precise language-motion mapping to enhance the navigation capabilities of legged robots in dynamic and unstructured environments [4][6][23]. Group 1: LOVON Framework Overview - LOVON addresses the challenges of long-range multi-target navigation for legged robots in complex environments, overcoming limitations of traditional methods that struggle with real-time visual disturbances and target loss [3][6]. - The framework combines task planning capabilities of large language models with open vocabulary visual detection, enabling robots to efficiently navigate and track dynamic targets in open-world scenarios [4][6][10]. Group 2: Key Features of LOVON - LOVON consists of three core modules that create a closed loop of language, vision, and motion, enhancing the robot's ability to perform complex tasks [10]. - The framework employs Laplacian variance filtering technology to stabilize visual processing, improving the detection frame rate by 25% during robot movement [12][13]. - An adaptive execution logic allows robots to respond to unexpected situations, such as target loss or external interference, by switching to search mode or seamlessly executing new commands [14][16]. Group 3: Performance Metrics - In simulated environments, LOVON achieved a success rate (SR) of 1.00, significantly outperforming traditional methods like EVT, which had an SR of 0.94 [19]. - The training efficiency of LOVON is remarkable, requiring only 1.5 hours to complete training, compared to 360 hours for the best competing model, TrackVLA, representing a 240-fold improvement [19][20]. Group 4: Practical Applications - LOVON's "plug-and-play" feature allows easy deployment on various mainstream legged robot platforms, supporting applications in home services, industrial inspections, and field research [21][24]. - The framework demonstrates exceptional capabilities in open-world adaptation, multi-target long-range tracking, robustness in dynamic environments, and resistance to interference, making it suitable for diverse real-world scenarios [24].