By making VLA models steerable at the primitive-action level, you can create a self-improving loop where robots identify skill gaps, practice autonomously, and expand their capabilities continuously.
InSight enables vision-language-action models to autonomously learn new manipulation skills by breaking down demonstrations into reusable primitive actions (like "move gripper to bowl").