点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要 的。 最近看到了很多HR的职位需求，list很长，但VLA算法是很"急需"。这一点，也体现在论文数量上。每天带着很多 小朋友看论文，也几乎都和VLA"挂钩"。 ❝ 但VLA貌似"很伤"，不好调，数据采集麻烦。这个事情，是很多同学持续在吐槽的。 只看论文而没有真机实验，在仿真里面做了好久，也不知道动起来啥样子。确实，具身和其它领域都有所不同，太 注重本体，即使是算法也极其依赖硬件。 ❝ 不少同学说，相当多的时间"浪费"在踩坑上了。 确实，真实数据采集上，需要借助硬件完成，比如遥操、VR、全身动补等方式。仿真和互联网数据，在泛化性能上 依然得不到保证，很多具身公司坚持"真机数据"路线。但真机数据采的数据并不好用，该怎么办？一轮下来又需要 好久。 模型优化上也很难顶，有的效果就是调不出，或者说训练不出效果。有些算法就是没效果，不知道怎么分析，真机 上一塌糊涂 ...

Core Viewpoint - The article emphasizes the increasing demand for VLA (Vision-Language Alignment) algorithms in the industry, highlighting the challenges faced by practitioners in data collection and model optimization [2][4]. Group 1: Industry Demand and Challenges - There is a significant demand for VLA algorithms, as reflected in the numerous job postings and research papers related to this field [2]. - Practitioners often face difficulties with VLA due to complex data collection processes and the reliance on hardware, leading to frustrations about wasted time and ineffective model training [2][4]. - Many companies in the embodied intelligence sector are committed to using real machine data, but the quality of this data can be suboptimal, complicating the training process [2][4]. Group 2: Educational Initiatives - The article introduces a practical course aimed at addressing the learning curve associated with VLA, developed in collaboration with industry experts [5]. - The course covers a comprehensive curriculum, including hardware, data collection, VLA algorithms, and real-world applications, designed to facilitate effective learning [8][9]. - Participants in the course will receive a SO-100 robotic arm, enhancing hands-on experience and practical application of the learned concepts [9]. Group 3: Course Structure and Content - The course is structured into nine chapters, covering topics from VLA basics to advanced model deployment and evaluation [11][12][13][14][15][16][17][18]. - Key areas of focus include data acquisition, model training, simulation environments, and the integration of VLA with world models [8][9][11][12][13][14][15][16][17]. - The course aims to equip learners with the necessary skills to transition into roles as algorithm engineers with 1-2 years of experience upon completion [25].