8个实战，彻底讲清VLA的各类方案

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) field, emphasizing the importance of real machine data and practical experience in achieving effective results in embodied intelligence applications. Group 1: Data Collection - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with remote operation, VR, and full-body motion capture being key techniques [8][9] - The quality of data collected is crucial, and methods like real2sim2real are highlighted as important for effective data acquisition [8] Group 2: VLA Training - Before deploying models in real machines, simulation debugging is essential, especially when real machine data is insufficient [10] - Training techniques are critical, with challenges in fine-tuning models and achieving good results with small data sets [10] - Some algorithms, like ACT, are easier to train, while others, such as π0 and π0.5, require more intricate techniques and experience [10] Group 3: VLA Deployment - After training, models often need to be "slimmed down" due to their large parameter sizes, which poses challenges for deployment on edge chips [12] - Techniques like quantization and distillation are necessary to minimize parameter size while maintaining performance [12] Group 4: Educational Initiatives - The article introduces a practical course aimed at helping individuals learn VLA effectively, covering various aspects such as hardware, data collection, algorithms, and deployment [13][16] - The course is designed for a wide audience, including students and professionals looking to transition into the embodied intelligence field [27]