在机器人操作任务中，预测性策略近年来在具身人工智能领域引起了广泛关注，因为它能够利用预测状态来提升机器人的操作性能。然而，让世界模型预测机器 人与物体交互的精确未来状态仍然是一个公认的挑战，尤其是生成高质量的像素级表示。 为解决上述问题， 国防科大、北京大学、深圳大学团队 提出 LaDi-WM（Latent Diffusion- based World Models） ，一种基于隐空间扩散的世界模型，用于预测隐 空间的未来状态。 具体而言，LaDi-WM 利用预训练的视觉基础模型 (Vision Fundation Models) 来构建隐空间表示，该表示同时包含几何特征（基于 DINOv2 构造）和语义特征（基于 Siglip 构造），并具有广泛的通用性，有利于机器人操作的策略学习以及跨任务的泛化能力。 编辑丨机器之心 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 基于 LaDi-WM，团队设计了一种扩散策略，该策略通过整合世界模型生成的预测状态来 ...

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].