VLA工作正在呈现爆发式增长.......

Core Viewpoint - The article discusses the rapid growth and potential of VLA (Whole Body Visual Learning) algorithms in the field of embodied intelligence, highlighting the increasing availability of diverse data sources and standardized evaluation metrics, which may lead to industrialization soon [2][12]. Group 1: VLA Development and Challenges - VLA algorithms are experiencing explosive growth, supported by various frameworks and tools like reinforcement learning (RL) that enhance their generalization performance [2]. - Despite the promising direction, many practitioners face challenges with VLA, including difficulties in tuning and data collection, leading to frustrations among newcomers in the field [3][10]. - Real data collection is essential, often requiring hardware setups such as remote operation and VR, but the quality of real-world data can be suboptimal, complicating the training process [5][11]. Group 2: VLA Implementation Modules - The implementation of VLA involves several key modules, including data collection methods based on imitation learning and reinforcement learning, with a focus on ensuring high-quality data [13]. - Training VLA models typically requires simulation debugging, especially when real-world data is insufficient, with frameworks like Mujoco and Isaac Gym being crucial for this process [14]. - After training, VLA models need to undergo a "slimming" process to reduce parameter size for deployment, which involves techniques like quantization and distillation to maintain performance while minimizing resource usage [15]. Group 3: Educational Initiatives - To address the learning curve associated with VLA technologies, a specialized course has been developed, focusing on practical skills and project experience in the field of embodied intelligence [16][19]. - The course covers a comprehensive curriculum, including hardware, data collection, VLA algorithms, evaluation, simulation, and real-world experiments, aimed at equipping participants with the necessary skills for the industry [21][36].