只用SO-100可以完成π0和π0.5的效果吗？

Core Viewpoint - The article discusses the challenges and complexities faced by beginners in implementing VLA (Vision-Language Alignment) models, emphasizing the need for practical experience and effective training methods to achieve successful deployment in real-world applications [2][4]. Group 1: Challenges in VLA Implementation - Many students report difficulties in achieving effective results with open-source models like GR00T and PI0, despite low training loss in simulations [2][4]. - The transition from simulation to real-world application (sim2real) poses significant challenges, particularly in data collection and model training [6][7]. - Beginners often struggle with the intricacies of data collection, model training, and deployment, leading to frustration and lack of progress [4][10]. Group 2: VLA Model Components - Data collection methods for VLA primarily include imitation learning and reinforcement learning, with a focus on high-quality data acquisition [6]. - Training VLA models typically requires simulation debugging and fine-tuning, especially when real-world data is limited [7]. - Deployment of VLA models necessitates optimization techniques such as model compression to ensure efficient performance on edge devices [9]. Group 3: Educational Initiatives - The article introduces a practical course aimed at helping students effectively learn VLA, covering various aspects such as hardware, data collection, algorithms, and real-world experiments [10][12]. - The course is designed for individuals seeking to enter the field of embodied intelligence, providing hands-on experience and project support [22][25]. - The course will commence on December 30, 2025, and includes a comprehensive curriculum to enhance participants' skills in VLA [23][26].