夹钢笔、叠杯子，VLA算法实战小班课来了~

Core Viewpoint - The article discusses the challenges and advancements in the VLA (Variable Learning Algorithm) models, emphasizing the importance of real machine data and practical experience in achieving effective results in embodied intelligence applications [2][4]. Group 1: Data Collection - Data collection methods primarily include imitation learning and reinforcement learning, with remote operation, VR, and full-body motion capture being key techniques [6][7]. - Ensuring high-quality data and effective data collection is crucial, particularly in the context of sim2real applications [7]. Group 2: VLA Training - Prior to real machine deployment, simulation debugging is essential, especially when real machine data is insufficient, making frameworks like Mujoco and Isaac Gym important [9]. - Training techniques are critical, with challenges in fine-tuning models and achieving good results with small data sets; models like π0 and π0.5 require high attention to detail and experience [9][10]. Group 3: VLA Model Deployment - After training, models need to undergo a "slimming" process due to their typically large parameter sizes, which poses challenges for deployment on edge chips; techniques like quantization and distillation are necessary [11]. Group 4: Educational Initiatives - The article introduces a practical course aimed at helping students effectively learn VLA, covering various aspects such as hardware, data collection, algorithms, evaluation, simulation, and deployment [12][14]. - The course is designed for individuals seeking to enter the embodied intelligence field, including students and professionals transitioning from traditional CV, robotics, or autonomous driving sectors [24].