Core Insights - The article introduces OmniAgent, a proactive perception agent developed by Zhejiang University, West Lake University, and Ant Group, addressing pain points in cross-modal alignment and fine-grained understanding in end-to-end omni-modal models [2][7][19] - OmniAgent employs an innovative "think-act-observe-reflect" closed-loop mechanism, transitioning from passive response to active inquiry, which enhances its performance in audiovisual understanding tasks [10][19] Background and Pain Points - End-to-end omni-modal models face high training costs and challenges in cross-modal feature alignment, leading to subpar performance in fine-grained cross-modal understanding [7] - Fixed workflow-based agents rely on rigid, human-defined processes, lacking the flexibility to autonomously plan and gather information based on questions [7] Methodology - OmniAgent's methodology includes a strategic scheduling of video and audio understanding capabilities within an iterative reflection loop, effectively overcoming cross-modal alignment challenges [8][15] - The agent autonomously decides whether to "listen" or "watch" based on the analysis of the question, utilizing a variety of multimodal tools for efficient information retrieval [15] Performance Results - OmniAgent achieved state-of-the-art (SOTA) results in multiple audiovisual understanding benchmarks, with an accuracy of 82.71% on the Daily-Omni Benchmark, surpassing Gemini 2.5-Flash (72.7%) and Qwen3-Omni-30B (72.08%) by over 10% [13] - In the OmniVideoBench, OmniAgent reached an accuracy of 59.1% in long video understanding tasks, significantly outperforming Qwen3-Omni-30B (38.4%) [13] Future Vision - The design of OmniAgent is highly extensible, allowing for the integration of additional modal tools [19] - OmniAgent is positioned to assist in generating high-quality COTT data for the development of next-generation omni-modal models capable of self-tool invocation [19]

Core Insights - The article discusses the transformative impact of Multimodal Fusion and Vision-Language Models (VLMs) on robot vision, enabling robots to evolve from simple mechanical executors to intelligent partners capable of understanding and interacting with complex environments [3][4][5]. Multimodal Fusion in Robot Vision - Multimodal fusion integrates various data types such as RGB images, depth information, LiDAR point clouds, language, and tactile data, significantly enhancing robots' perception and understanding of their surroundings [3][4][9]. - The main fusion strategies have evolved from early explicit concatenation to implicit collaboration within unified architectures, improving feature extraction and task prediction [10][11]. Applications of Multimodal Fusion - Semantic scene understanding is crucial for robots to recognize objects and their relationships, where multimodal fusion greatly improves accuracy and robustness in complex environments [9][10]. - 3D object detection is vital for autonomous systems, combining data from cameras, LiDAR, and radar to enhance environmental understanding [16][19]. - Embodied navigation allows robots to explore and act in real environments, focusing on goal-oriented, instruction-following, and dialogue-based navigation methods [24][26][27][28]. Vision-Language Models (VLMs) - VLMs have advanced significantly, enabling robots to understand spatial layouts, object properties, and semantic information while executing tasks [46][47]. - The evolution of VLMs has shifted from basic models to more sophisticated systems capable of multimodal understanding and interaction, enhancing their applicability in various tasks [53][54]. Future Directions - The article identifies key challenges in deploying VLMs on robotic platforms, including sensor heterogeneity, semantic discrepancies, and the need for real-time performance optimization [58]. - Future research may focus on structured spatial modeling, improving system interpretability, and developing cognitive VLM architectures for long-term learning capabilities [58][59].

Core Viewpoint - The article discusses the advancements in multimodal fusion and vision-language models (VLMs) in robot vision, emphasizing their role in enhancing robots' perception and understanding capabilities in complex environments [4][5][56]. Multimodal Fusion in Robot Vision Tasks - Semantic scene understanding is a critical task in visual systems, where multimodal fusion significantly improves accuracy and robustness by integrating additional information such as depth and language [9][11]. - Current mainstream fusion strategies include early fusion, mid-level fusion, and late fusion, evolving from simple concatenation to more sophisticated interactions within a unified architecture [10][12][16]. Applications of Multimodal Fusion - In autonomous driving, 3D object detection is crucial for accurately identifying and locating pedestrians, vehicles, and obstacles, with multimodal fusion enhancing environmental understanding [15][18]. - The design of multimodal fusion involves addressing when to fuse, what to fuse, and how to fuse, with various strategies impacting performance and computational efficiency [16][17]. Embodied Navigation - Embodied navigation allows robots to explore and act in real environments, focusing on autonomous decision-making and dynamic adaptation [23][25][26]. - Three representative methods include goal-directed navigation, instruction-following navigation, and dialogue-based navigation, showcasing the evolution from perception-driven to interactive understanding [25][26][27]. Visual Localization and SLAM - Visual localization determines a robot's position, which is challenging in dynamic environments; recent methods leverage multimodal fusion to improve performance [28][30]. - SLAM (Simultaneous Localization and Mapping) has evolved from geometric-driven to semantic-driven approaches, integrating various sensor data for enhanced adaptability [30][34]. Vision-Language Models (VLMs) - VLMs have progressed significantly, focusing on semantic understanding, 3D object detection, embodied navigation, and robot operation, with various fusion methods being explored [56][57]. - Key innovations in VLMs include large-scale pre-training, instruction fine-tuning, and structural optimization, enhancing their capabilities in cross-modal reasoning and task execution [52][53][54]. Future Directions - Future research should focus on structured spatial modeling, improving system interpretability and ethical adaptability, and developing cognitive VLM architectures for long-term learning [57][58].