地瓜精酿馆开张大吉：碰杯VLA观点，互诉机器人信仰｜地瓜机器人x锦秋基金

Core Insights - The article discusses the evolving landscape of robotics, highlighting the importance of collaboration among industry players and the need for innovative solutions in the field [2][14]. Group 1: Industry Challenges - There is a lack of foundational data in robotics compared to other fields like the internet and autonomous driving, which hampers the development of embodied interaction platforms [18]. - Current training methods for VLA (Vision-Language Agents) rely heavily on superficial data, lacking essential physical constraints such as dynamics and collision, leading to instability in practical applications [18]. - The engineering challenges persist, with the need for parameter tuning in both dynamic models and reward systems, resulting in lengthy and costly training-validation cycles [18]. Group 2: Development Strategies - Short-term implementation of VLA is hindered by the absence of time and constraint concepts in the "brain" outputs, necessitating a clean-up and constraint layer for planning and control [18]. - A rule-based safety net is recommended for controlled environments, combining rules with learnable algorithms for optimization, allowing for initial commercial delivery while gradually building data loops and capabilities [18]. - The advancement of VLA requires addressing two key factors: the shortage of talent in foundational model development and the lack of entities capable of commercializing these models [18]. Group 3: Future Directions - A dual approach is suggested, where upper-level large models handle understanding and task decomposition, while lower-level reinforcement learning and control ensure constraint satisfaction and real-time stability [18]. - The use of reinforcement learning combined with physical simulation is proposed to generate data and learn strategies, akin to how children learn to walk through trial and error [18]. - There is optimism for the long-term potential of learning-based control systems, which, despite being in early stages, possess the ability to generalize and adapt effectively [18].