点击下方 卡片 ，关注" 具身智能 之心 "公众号 在机器人操作领域，Vision-Language-Action（VLA）模型虽已展现出一定技术潜力，但其在 复杂推理 与 长程任务规划 场景下的性能，仍受限于数据稀缺与模型 容量两大核心问题。为此，我们提出了 ManiAgent —— 一种面向通用机器人操作任务的智能体架构，该架构可实现从 任务描述、环境输入 到 机器人操作动作 的 端到端输出 。 在 ManiAgent 框架中，多个智能体通过协同交互分别承担环境感知、子任务分解与动作生成功能，能够高效应对复杂操作场景。我们通过实验评估发现， ManiAgent 在 SimplerEnv 基准测试中的任务成功率达 86.8%， 在 真实世界拾取 - 放置任务 中的成功率更高达 95.8%。 值得注意的是，依托其高任务成功率， ManiAgent 还可作为 高效数据采集工具 ，基于该工具获取的训练数据所构建的 VLA 模型，性能能够与基于人工标注数据集训练的 VLA 模型相媲美，这为机器 人操作领域的技术优化与落地提供了重要支撑。 图1: ManiAgent的整体工作流程示例 论文题目：ManiAgent: ...

Core Insights - The article discusses the rapid advancements in artificial intelligence, particularly in embodied intelligence, which connects cognition and action, emphasizing the importance of robot manipulation in achieving general artificial intelligence (AGI) [5][9]. Summary by Sections Overview of Robot Manipulation - The paper titled "Towards a Unified Understanding of Robot Manipulation: A Comprehensive Survey" provides a comprehensive overview of the field of robot manipulation, detailing the evolution from rule-based control to intelligent control systems that integrate reinforcement learning and large models [6][10]. Key Challenges in Embodied Intelligence - Robot manipulation is identified as a core challenge in embodied intelligence due to its requirement for seamless integration of perception, planning, and control, which is essential for real-world interactions in diverse and unstructured environments [9][10]. Unified Framework - A unified understanding framework is proposed, which expands the traditional high-level planning and low-level control paradigm to include language, code, motion, affordance, and 3D representation, enhancing the semantic decision-making role of high-level planning [11][21]. Classification of Learning Control - A novel classification method for low-level learning control is introduced, dividing it into input modeling, latent learning, and policy learning, providing a systematic perspective for research in low-level control [24][22]. Bottlenecks in Robot Manipulation - The article identifies two major bottlenecks in robot manipulation: data collection and utilization, and system generalization capabilities, summarizing existing research progress and solutions for these challenges [27][28]. Future Directions - Four key future directions are highlighted: building a true "robot brain" for general cognition and control, breaking data bottlenecks for scalable data generation and utilization, enhancing multimodal perception for complex object interactions, and ensuring human-robot coexistence safety [35][33].

Core Insights - The article discusses the rapid advancements in artificial intelligence, particularly in embodied intelligence, which connects cognition and action, emphasizing the importance of robot manipulation in achieving general artificial intelligence (AGI) [3][4]. Summary by Sections Overview of Embodied Intelligence - Embodied intelligence is highlighted as a crucial frontier that enables agents to perceive, reason, and act in real environments, moving from mere language understanding to actionable intelligence [3]. Paradigm Shift in Robot Manipulation - The research in robot manipulation is undergoing a paradigm shift, integrating reinforcement learning, imitation learning, and large models into intelligent control systems [4][6]. Comprehensive Survey of Robot Manipulation - A comprehensive survey titled "Towards a Unified Understanding of Robot Manipulation" systematically organizes over 1000 references, covering hardware, control foundations, task and data systems, and cross-modal generalization research [4][6][7]. Unified Framework for Understanding Robot Manipulation - The article proposes a unified framework that extends traditional high-level planning and low-level control classifications, incorporating language, code, motion, affordance, and 3D representations [9][20]. Key Bottlenecks in Robot Manipulation - Two major bottlenecks in robot manipulation are identified: data collection and utilization, and system generalization capabilities, with a detailed analysis of existing solutions [27][28]. Future Directions - Four key future directions are proposed: building a true "robot brain" for general cognition and control, breaking data bottlenecks for scalable data generation and utilization, enhancing multi-modal perception for complex interactions, and ensuring human-robot coexistence safety [34].

Core Viewpoint - The article discusses the limitations of current robotics technology, emphasizing that while hardware has advanced significantly, the real challenge lies in robots' ability to understand and predict physical interactions in the world, which is essential for practical applications in everyday environments [2][20]. Group 1: Learning-Based Dynamics Models - The article reviews the application of learning-based dynamics models in robotic operations, focusing on how these models can predict physical interactions from sensory data, allowing robots to perform complex tasks [8][20]. - Learning-based dynamics models face challenges in designing efficient state representation methods, which directly impact the model's generalization ability and data efficiency [9][20]. - Various state representation methods are discussed, including raw sensory data, latent representations, particle representations, keypoint representations, and object-centric representations, each with its advantages and disadvantages [10][11][17][20]. Group 2: Integration with Control Methods - The article explores how dynamics models can be integrated with control methods, particularly in motion planning and policy learning applications, enabling robots to autonomously plan and adjust operations in complex environments [12][14][20]. - Motion planning optimizes paths or trajectories to guide robots in task execution without precise models, while policy learning directly maps sensory data to action strategies [13][14]. Group 3: Future Research Directions - Future research will focus on enhancing the robustness of learning models, especially in partially observable and complex environments, with multi-modal perception and uncertainty quantification being key areas of exploration [15][16][20]. - The article highlights the importance of state representation methods in improving the performance of learning-based dynamics models, emphasizing the need for structured prior knowledge to efficiently process information [24][25][20].

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 Insights - The article focuses on advancements in embodied intelligence and robotic manipulation, highlighting various research projects and methodologies aimed at improving robot learning and performance in real-world tasks [2][3][4]. Group 1: 2025 Research Highlights - Numerous projects are set for 2025, including "Steering Your Diffusion Policy with Latent Space Reinforcement Learning" and "Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation," which aim to enhance robotic capabilities in manipulation and interaction [2]. - The "BEHAVIOR Robot Suite" aims to streamline real-world whole-body manipulation for everyday household activities, indicating a focus on practical applications of robotic technology [2]. - "You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations" emphasizes the potential for robots to learn complex tasks from minimal demonstrations, showcasing advancements in imitation learning [2]. Group 2: Methodological Innovations - The article discusses various innovative methodologies such as "Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning," which aims to improve the adaptability of robots in different environments [2]. - "Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion" highlights the focus on enhancing dexterity in robotic hands, crucial for complex manipulation tasks [4]. - "Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation" indicates a trend towards using synthetic data to train robots, which can significantly reduce the need for real-world data collection [7]. Group 3: Future Directions - The research agenda for 2024 and beyond includes projects like "Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching," which suggests a shift towards utilizing advanced data representations for improved learning outcomes [9]. - "Zero-Shot Framework from Image Generation World Model to Robotic Manipulation" indicates a future direction where robots can generalize from visual data without prior specific training, enhancing their versatility [9]. - The emphasis on "Human-to-Robot Data Augmentation for Robot Pre-training from Videos" reflects a growing interest in leveraging human demonstrations to improve robotic learning efficiency [7].