Engineer explains how Vision Language Actions work as natural extensions of sequence modeling to enable robots to understand and execute complex tasks.

VLMs are being repurposed into control policies that can enhance existing robots with open models like openVLA and gr00t

Action tokenization versus continuous control represents a fundamental architectural choice in VLA development

The real bottlenecks in VLA development are data collection and embodiment challenges, not just model scaling

VLAs function as sequence models similar to GPT but extended to control robotic outputs like torque and acceleration