深大团队让机器人听懂指令精准导航，成功率可达72.5%，推理效率提升40%

Core Insights - The article discusses the introduction of a new framework called UNeMo for visual-language navigation (VLN), developed by a team led by Professor Li Jianqiang from Shenzhen University in collaboration with Beijing Institute of Technology and Moscow University [1][3]. Group 1: Framework Overview - UNeMo utilizes a multi-modal world model (MWM) and a hierarchical predictive feedback navigator (HPFN) to enhance the decision-making capabilities of navigation agents by allowing them to predict future visual states based on current visual features and language instructions [6][12]. - The framework addresses the disconnection between language reasoning and visual navigation, which has been a significant challenge in embodied AI [6][8]. Group 2: Performance Metrics - UNeMo achieves a navigation success rate of 72.5% in unseen environments, outperforming the existing method NavGPT2, which has a success rate of 71% [15]. - The framework demonstrates a significant reduction in resource consumption, with GPU memory usage dropping from 27GB to 12GB (a 56% reduction) and inference speed improving by 40% [15]. Group 3: Experimental Validation - In experiments on the R2R dataset, UNeMo shows a balance between lightweight configuration and high-performance decision-making, achieving a path efficiency (SPL) improvement from 60% to 61.3% [15]. - UNeMo exhibits a notable advantage in long-path navigation, with a success rate increase of 5.6% for paths longer than 7 units, compared to a mere 1.2% increase for shorter paths [17]. Group 4: Scalability and Adaptability - The framework has been tested across various navigation baselines and datasets, demonstrating its adaptability and scalability beyond LLM-based systems [20]. - UNeMo's collaborative training architecture allows it to effectively apply to different types of navigation tasks, enhancing its overall utility in practical applications [20].