具身导航

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上海交大具身导航中的感知智能、社会智能和运动智能全面综述
具身智能之心· 2025-09-02 00:03
Core Insights - The article presents the TOFRA framework, which decomposes the embodied navigation process into five key stages: Transition, Observation, Fusion, Reward-policy construction, and Action execution, providing a structured analysis for embodied navigation research [2][14] - It systematically integrates research findings from computer vision, classical robotics, and bionics in the context of embodied navigation, highlighting the complementary nature of these fields in sensing intelligence, social intelligence, and motion intelligence [2][3] - The article identifies four core challenges in the field of embodied navigation: adaptive spatiotemporal scale, joint optimization, system integrity, and data task generalization, guiding future research directions [2][3] Group 1: Research Background - Embodied Artificial Intelligence (EAI) emphasizes self-perception and interaction with humans or the environment as a pathway to Artificial General Intelligence (AGI) [2] - The core feature of embodied navigation is its egocentric perception and distributed computing capabilities, contrasting with traditional navigation methods that rely on predefined maps or external localization [2][3] Group 2: Intelligence Types - Sensing Intelligence: Achieved through multimodal self-centered perception, allowing for spatial cognition without complete reliance on pre-built global maps [3][4] - Social Intelligence: Enables understanding of high-level semantic instructions from humans, supporting complex task execution beyond predefined waypoints [10][11] - Motion Intelligence: Involves the ability to perform flexible and adaptive physical interactions in complex environments, not limited to fixed paths [10][11] Group 3: TOFRA Framework - Transition (T): Involves predicting the next state using internal sensors and various methods, including dynamics modeling and end-to-end neural networks [14][20] - Observation (O): Focuses on how robots perceive the environment through external sensors, forming an understanding of the external world [27][28] - Fusion (F): Combines internal state predictions with external perceptions to achieve optimal state estimation using classical Bayesian methods and neural networks [45][48] Group 4: Action Execution - Action execution involves the robot utilizing motion skills to complete the action sequences generated by the policy, including basic skills and complex skill combinations [60][61] - The article discusses the evolution of action execution from basic motion skills to complex combinations and morphological cooperation, highlighting the advancements in motion intelligence [60][68] Group 5: Application Scenarios - The TOFRA framework is applied to three typical navigation scenarios: embodied autonomous driving, indoor navigation, and complex terrain navigation, detailing how to integrate the framework's stages for efficient navigation systems [74][75][76]
最新综述!多模态融合与VLM在具身机器人领域中的方法盘点
具身智能之心· 2025-09-01 04:02
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].
具身目标导航/视觉语言导航/点导航工作汇总!
具身智能之心· 2025-08-12 07:04
Core Insights - The article discusses the development and methodologies related to embodied navigation, particularly focusing on point-goal navigation and visual-audio navigation techniques [2][4][5]. Group 1: Point-Goal Navigation - The comparison between model-free and model-based learning for point-goal navigation highlights the effectiveness of different approaches in planning and execution [4]. - RobustNav aims to benchmark the robustness of various embodied navigation methods, providing a framework for evaluating performance [5]. - Significant advancements in visual odometry techniques have been noted, showcasing their effectiveness in embodied point-goal navigation [5]. Group 2: Visual-Audio Navigation - The integration of audio-visual elements in navigation tasks is explored, emphasizing the importance of sound in enhancing navigation efficiency [7][8]. - Various projects and papers have been referenced that focus on audio-visual navigation, indicating a growing interest in multi-modal approaches [8][9]. - The development of platforms like SoundSpaces 2.0 aims to facilitate research in visual-acoustic learning, further bridging the gap between visual and auditory navigation [8]. Group 3: Object Goal Navigation - The article outlines several methodologies for object goal navigation, including modular approaches and self-supervised learning techniques [9][13]. - The importance of auxiliary tasks in enhancing exploration and navigation capabilities is emphasized, indicating a trend towards more sophisticated learning frameworks [13][14]. - Benchmarking efforts such as DivScene aim to evaluate large language models for object navigation, reflecting the increasing complexity of navigation tasks [9][14]. Group 4: Vision-Language Navigation - The article discusses advancements in vision-language navigation, highlighting the role of language in guiding navigation tasks [22][23]. - Techniques such as semantically-aware reasoning and history-aware multimodal transformers are being developed to improve navigation accuracy and efficiency [22][23]. - The integration of language with visual navigation is seen as a critical area of research, with various projects aiming to enhance the interaction between visual inputs and language instructions [22][23].
正式开课啦!具身智能目标导航算法与实战教程来了~
具身智能之心· 2025-07-25 07:11
Core Viewpoint - Goal-Oriented Navigation empowers robots to autonomously complete navigation tasks based on goal descriptions, marking a significant shift from traditional visual language navigation systems [2][3]. Group 1: Technology Overview - Embodied navigation is a core area of embodied intelligence, relying on three technical pillars: language understanding, environmental perception, and path planning [2]. - Goal-Oriented Navigation requires robots to explore and plan paths in unfamiliar 3D environments using only goal descriptions such as coordinates, images, or natural language [2]. - The technology has been industrialized across various verticals, including delivery, healthcare, and hospitality, with companies like Meituan and Aethon deploying autonomous delivery robots [3]. Group 2: Technological Evolution - The evolution of Goal-Oriented Navigation can be categorized into three generations: 1. First Generation: End-to-end methods focusing on reinforcement learning and imitation learning, achieving breakthroughs in Point Navigation and closed-set image navigation tasks [5]. 2. Second Generation: Modular methods that explicitly construct semantic maps, breaking tasks into exploration and goal localization [5]. 3. Third Generation: Integration of large language models (LLMs) and visual language models (VLMs) to enhance knowledge reasoning and open-vocabulary target matching [7]. Group 3: Challenges in Learning - Learning Goal-Oriented Navigation is challenging due to the need for knowledge across multiple domains, including natural language processing, computer vision, and reinforcement learning [9]. - The fragmented nature of knowledge and the abundance of literature can overwhelm beginners, making it difficult to extract frameworks and understand development trends [9]. Group 4: Course Offering - A new course has been developed to address the challenges in learning Goal-Oriented Navigation, focusing on quick entry, building a domain framework, and combining theory with practice [10][11][12]. - The course includes a comprehensive curriculum covering semantic navigation frameworks, Habitat simulation ecology, end-to-end navigation methodologies, modular navigation architectures, and LLM/VLM-driven navigation systems [13][16][17][19][20][23].