机器人需求驱动导航新SOTA，成功率提升15%！浙大&vivo联手打造

Core Viewpoint - The research team from Zhejiang University and vivo AI Lab has made significant progress in developing a cognitive-driven navigation framework called CogDDN, which enables robots to understand human intentions and navigate complex environments autonomously [2][5][33]. Research Motivation - As mobile robots become more integrated into daily life, there is a need for them to not only execute commands but also understand human needs, such as seeking food when a person feels hungry [5]. - Traditional demand-driven navigation methods rely heavily on extensive data training and struggle in unfamiliar environments or vague instructions, prompting the exploration of more generalizable navigation methods [6]. Framework Overview - The CogDDN framework is based on the dual-process theory from psychology, combining heuristic (System 1) and analytical (System 2) decision-making processes to simulate human-like reasoning in navigation tasks [8][20]. - The framework consists of three main components: a 3D robot perception module, a demand matching module, and a dual-process decision-making module [13]. 3D Robot Perception Module - The team utilized the state-of-the-art single-view 3D detection method, UniMODE, to enhance the robot's three-dimensional perception capabilities in indoor navigation [15]. Demand Matching Module - The demand matching module aligns objects with human needs based on shared characteristics, employing supervised fine-tuning techniques to improve the accuracy of recommendations in complex scenarios [16]. Dual-Process Decision Making - The heuristic process allows for quick, intuitive decision-making, while the analytical process focuses on error reflection and strategy optimization [9][23]. - The heuristic process includes two sub-modules: Explore, which generates exploratory actions to scan the environment, and Exploit, which focuses on precise actions to achieve navigation goals [19]. Experimental Results - In closed-loop navigation experiments using the AI2-THOR simulator, CogDDN outperformed existing state-of-the-art methods, achieving a navigation success rate (NSR) of 38.3% and a success rate for weighted path length (SPL) of 17.2% [26][27]. - The framework demonstrated superior adaptability and efficiency in unseen scenes compared to methods that rely solely on forward-facing camera inputs [28]. Continuous Learning and Adaptation - The analysis process in CogDDN allows for iterative learning, where the system reflects on obstacles encountered during navigation and integrates this knowledge into its decision-making framework [24][31]. - The reflection mechanism significantly enhances the system's performance in future navigation tasks, showcasing its robust learning capabilities [32]. Conclusion - CogDDN represents a significant advancement in cognitive-driven navigation systems, enabling robots to efficiently adapt and optimize their strategies in complex environments [33][34]. - The dual-process capability of CogDDN lays a solid foundation for the development of intelligent robotic technologies in demand-driven navigation tasks [35].