Core Viewpoint - The article discusses the rapid development and challenges of the VLA (Whole Body Visual Learning Algorithm) in the field of embodied intelligence, highlighting the importance of real data collection and the difficulties faced by newcomers in the field [2][3][4]. Group 1: VLA Development and Challenges - The VLA algorithm is experiencing explosive growth, with various frameworks and tools, such as reinforcement learning (RL), enhancing its performance [2]. - Data collection methods are diversifying, with millions of open-source data becoming available, indicating a potential for industrialization [2]. - Many practitioners express frustration with the challenges of tuning VLA models and the complexities of data collection, particularly for those new to the field [3][5]. Group 2: Data Collection and Training - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with a focus on remote operation and VR for mechanical arms [13]. - Simulation and real-to-sim-to-real (real2sim2real) techniques are crucial for training VLA models, especially when real data is insufficient [14]. - Training techniques are critical, with many practitioners struggling to achieve good results due to the complexity of models like π0 and π0.5, which require high attention to detail [14][10]. Group 3: Model Deployment - After training, VLA models require optimization to reduce their parameter size for deployment, which is essential for edge computing applications [15]. - Techniques such as quantization and distillation are necessary to maintain performance while minimizing model size [15]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn VLA effectively, covering hardware, data collection, algorithm deployment, and real-world experiments [17][20]. - The course is designed to save time and reduce the learning curve for newcomers, providing practical experience that can enhance resumes [18][31].