硬件不是问题，理解才是门槛：为什么机器人还没走进你家

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].