用低成本复现这几个Git上最受欢迎的VLA任务

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 beginners 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 mechanical 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 beginners on common pitfalls, particularly with models like π0 and π0.5, which require specific tricks for data collection and training [5]. Group 2: Course Offerings - The "Embodied Intelligence Heart" platform has successfully replicated methods such as ACT, GR00T, π0, and π0.5 using SO-100 and LeRobot, aiming to help those lacking access to expensive equipment [8]. - A new practical course titled "VLA Small Class for Practical and Job-Seeking" has been developed in collaboration with VLA experts to assist learners in effectively utilizing VLA technologies [9]. - 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-machine experiments [14]. Group 3: Course Details and Requirements - The course is designed for individuals seeking practical experience and projects in the VLA field, including students at various academic levels and those transitioning from traditional fields like computer vision and robotics [25]. - Participants will receive a SO-100 robotic arm as part of the course package, which includes both teaching and execution arms [18]. - The course aims to equip learners with skills equivalent to 1-2 years of experience as algorithm engineers upon completion [27].