中金：具身智能走向数据驱动 高价值信息量成具身智能竞争核心

Core Insights - The report from CICC highlights that the short-term layered architecture remains mainstream due to engineering controllability, while VLA shows potential in complex tasks and human-machine interaction. The world model is viewed as a long-term direction due to its cross-device transfer capability [1] Group 1: Embodied Intelligence Algorithms - Layered control serves as the foundational architecture paradigm, utilizing a two-tier structure for engineering [1] - The VLA paradigm, based on VLM, enhances generalization and interaction capabilities, representing an active research direction [1] - The world model provides physical constraints through environmental modeling and future predictions, currently in the research-led stage [1] Group 2: Embodied Intelligence Data - Robotic data encompasses multimodal sources, with industries seeking low-cost data acquisition and high-efficiency application paths [2] - Data acquisition methods include real machines, video (first-person/third-person), and simulation [2] - Data security is a critical baseline, with humanoid robot manufacturers facing challenges related to permission isolation, data encryption systems, and cross-border transmission policies [2] Group 3: Hot Topics in Embodied Intelligence - The Scaling Law for robots has not yet seen explosive breakthroughs, with limitations such as insufficient real data production capacity and Sim2Real transfer being key constraints [3] - Benchmarking is driving the standardization of evaluation processes, as embodied robots lack a recognized quantitative framework [3] - Physical AI, which integrates physical knowledge with AI models, has progressed to applications in robotic operations [3]