具身的半壁江山都在VLA了......

Core Viewpoint - The article emphasizes the increasing demand for VLA (Variable Learning Algorithm) in the industry, highlighting the challenges associated with data collection and model training, and the need for practical learning resources in this field [1][2][3]. Group 1: VLA Demand and Challenges - There is a significant demand for VLA algorithms in job postings, indicating a growing interest in this technology [1]. - Many practitioners express frustration with the difficulties in tuning VLA algorithms and the complexities of data collection [2]. - The reliance on real machine data for effective VLA model training poses challenges, as many companies struggle with the quality of the collected data [3]. Group 2: VLA Implementation Modules - The implementation of VLA involves several key modules, including data collection methods based on imitation learning and reinforcement learning [8]. - Training VLA models typically requires simulation debugging, especially when real machine data is insufficient, making simulation frameworks like Mujoco and Isaac Gym crucial [9]. - After training, VLA models often require optimization techniques such as quantization and distillation to reduce model size while maintaining performance [10]. Group 3: Educational Resources and Courses - The article introduces a practical course aimed at helping individuals learn VLA effectively, addressing the rapid updates in technology and the challenges faced by learners [11]. - The course covers a comprehensive curriculum, including mechanical arm hardware, data collection, VLA algorithms, evaluation, simulation, and deployment [16][17]. - Participants will receive hands-on experience with real hardware, enhancing their learning and practical skills in the VLA domain [28].