Twenty-Eight Thousand Drawings Test What the Robot Actually Learned

Robot data is expensive, so it is difficult to study how behavior diversity changes as demonstrations scale. BURGER starts each task from a blank canvas and asks a robot arm to draw an object or spatial composition described in language, allowing many valid strokes and orders. Its prompt-driven pipeline generated 28,200 simulated demonstrations across 282 tasks. Evaluations of five vision-language-action models expose different failures in language grounding, spatial composition and motion generation; some systems benefited from greater diversity and scale and generalized to unseen compositions of familiar objects. A trained policy was also deployed on a Franka Panda arm and could draw reactively during human intervention. Drawing is a controlled proxy, not a general test of manipulation.