都在说VLA，很多同学连demo都跑不好......

Core Viewpoint - The article discusses the challenges and advancements in the field of VLA (Vision-Language Alignment) models, emphasizing the importance of real machine data and practical applications in robotics and embodied intelligence. Group 1: Challenges in VLA Implementation - Many students struggle with the transition from theoretical knowledge to practical application, often finding it difficult to achieve satisfactory results without hands-on experience [2][6] - The reliance on real machine data for effective training and deployment of VLA models is highlighted, with a focus on the limitations of simulation data [2][8] Group 2: Data Collection and Training - Data collection methods for VLA include imitation learning and reinforcement learning, with a particular emphasis on remote operation and VR techniques [8] - The training of VLA models requires careful tuning and optimization, with specific challenges noted for models like π0 and π0.5, which demand a high level of expertise [10][12] Group 3: Deployment and Optimization - Post-training, VLA models often require optimization techniques such as quantization and distillation to reduce parameter size while maintaining performance [12] - The deployment of VLA models on edge devices presents significant challenges due to their typically large parameter sizes [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, algorithm implementation, and real-world applications [14][30] - The course is designed for a diverse audience, including students and professionals looking to transition into the field of embodied intelligence [27][30]