VLA models show promise in learning scalable, language-conditioned robotic behaviors with multimodal action generation, but their success depends on task diversity, data scale, and how well they ground spatial language into continuous control.
BURGER is a robotic drawing benchmark that tests how well vision-language-action models learn diverse behaviors from scaled training data.