首个面向求职+工业级的VLA实战教程！真机+各类VLA算法部署+量化+世界模型

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data collection and the complexities involved in model training and deployment. Group 1: Data Collection - Real machine data collection is crucial for VLA models, with methods including remote operation, VR, and full-body motion capture being highlighted as effective approaches [2][8]. - The article stresses the need for high-quality data and the significance of the real2sim2real process in ensuring effective data collection [8]. Group 2: Model Training - Training VLA models typically requires simulation debugging before real machine deployment, especially when real machine data is insufficient [10]. - The article notes that many beginners struggle with model training, particularly with advanced models like π0 and π0.5, which require specific techniques and experience to achieve good results [6][10]. Group 3: Model Deployment - After training, VLA models often need to undergo a "slimming" process due to their large parameter sizes, which poses challenges for deployment on edge devices [12]. - Techniques such as quantization and distillation are essential to minimize parameter size while maintaining performance [12]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn about VLA, covering various aspects such as hardware, data collection, algorithms, and deployment [14][16]. - The course is designed for a wide audience, including students and professionals looking to transition into the VLA field, and includes hands-on experience with hardware [27][30].