Core Viewpoint - The article discusses the implementation challenges and advancements of VLA (Variable Latent Action) algorithms and Reinforcement Learning (RL) in robotics, focusing on their practical applications and future developments in the field of embodied intelligence [3][13]. Group 1: Guest Speakers - Wei Sui, Vice President of Diguo Robotics, has extensive experience in developing 2.5D and 3D vision algorithms for robotics and autonomous driving, leading a team that created a comprehensive 4D labeling system, with millions of chips shipped [5]. - Zhang Qiang, Chief Researcher and Academic Committee Director at Beijing Humanoid Robotics, specializes in humanoid robot motion control and multimodal perception, contributing to the development of core RL algorithms for humanoid robots [6][8]. - Wang Tiancai, Partner at Yuanli Lingji, has published over 30 papers in top international conferences and is a core author of notable algorithms in end-to-end autonomous driving [9][10]. - Yu Chao, Assistant Professor at Tsinghua Shenzhen Research Institute, focuses on decision intelligence driven by reinforcement learning, with over 50 published papers and significant academic recognition [11][12]. Group 2: Key Topics Discussed - The article addresses the pain points in the architecture and models of VLA, exploring how to enhance the overall motion control of robots [16]. - It discusses the integration of VLA with RL for better real-world application, including considerations for hardware selection and lightweight implementations [16].