成本仅2k！完成各类VLA任务的复现

Core Viewpoint - The article discusses the challenges faced by beginners in the field of VLA (Vision-Language Alignment) tasks due to high costs and the complexity of data collection and model training, while introducing a comprehensive course aimed at addressing these issues and providing practical skills for aspiring professionals in the field [3][5][9]. Group 1: Challenges in VLA Tasks - Many students express frustration over the high costs associated with mechanical arms and sensors, which can exceed 15,000 yuan, making it difficult for self-learners or those without equipment to engage in VLA tasks [3]. - Open-source low-cost robotic arms are available, but many beginners struggle to achieve effective results due to difficulties in data collection and model training [4]. - A significant amount of time is wasted by students on troubleshooting and overcoming obstacles in data collection, model training, and deployment, particularly with complex models like π0 and π0.5, and GR00T [5]. Group 2: Course Offerings - The "Embodied Intelligence Heart" platform has replicated methods such as ACT, GR00T, π0, and π0.5 using SO-100 and LeRobot to help students who lack access to expensive equipment and do not know how to get started [8]. - A comprehensive VLA practical course has been developed in collaboration with industry experts, focusing on real-world applications and job readiness [9][14]. - The course covers a wide range of topics, including hardware for robotic arms, data collection, VLA algorithms, evaluation, simulation, deployment of mainstream VLA models, and various real-world experiments [14][15]. Group 3: Course Details and Requirements - Students who purchase the course will receive a SO-100 robotic arm, which includes both teaching and execution arms, delivered directly to them [18]. - The course is designed for individuals seeking practical experience and projects in the VLA field, including those transitioning from traditional computer vision, robotics, or autonomous driving [25]. - The course requires a foundational knowledge of Python and Pytorch, as well as experience in debugging real machines and data collection [25].