Core Insights - The article discusses the advancements in embodied intelligence, particularly focusing on the new training paradigm introduced by Zhujidongli with their LimX DreamActor, which utilizes a multi-data approach for training robots [1][3][25]. Group 1: LimX DreamActor Overview - LimX DreamActor integrates video data, simulation data, and real machine data to enhance robot training efficiency and performance [3][17]. - The training process consists of four steps: data collection using consumer-grade devices, 3D reconstruction with physical parameters, extensive training in simulated environments, and fine-tuning on real machines [7][9][15]. Group 2: Data Utilization Strategy - The multi-data strategy addresses the limitations of each data type: real machine data is expensive, simulation data lacks realism, and video data is challenging to apply due to the absence of physical properties [3][17]. - The approach emphasizes data efficiency, aiming to achieve better performance at lower costs by leveraging diverse data sources [3][16]. Group 3: Technical Innovations - DreamActor employs advanced real machine reinforcement learning (RL) techniques, which significantly enhance learning efficiency and the ability to generalize from simulation to real-world applications [16][18]. - The integration of Real2Sim2Real strategies allows for a more reliable deployment of robots, reducing risks and shortening development cycles [18][20]. Group 4: Historical Context and Evolution - LimX DreamActor is an evolution of previous efforts by Zhujidongli, such as LimX VGM, which focused solely on video data for training robots without real machine samples [21][23]. - The transition from VGM to DreamActor reflects a deeper understanding of data application and the pursuit of optimal data-performance ROI [21][23]. Group 5: Industry Implications - The advancements in the multi-data approach are expected to lower the barriers for participation in embodied intelligence development, enabling more teams to engage in this field [25]. - The article suggests that achieving a balance between efficiency and stability in robot training is crucial for the large-scale application of embodied intelligence [25].