first two-thirds of the provided demonstration, and treat the last one-third as the ground truth for the LLM
to predict. Larger models quantitatively perform better with generally lower variance (see Appendix).
In Whiteboard Drawing, the goal is to continue a scripted demonstration of drawing loops on a whiteboard
(see Fig. 2). Loops are defined by parametric equations of the form x = ax cos(bt) + dx and y =
aysin(bt)+cyt+dy. We execute the motions using position control and record the end-effector positions at
5 Hz, then discretize states in between 0 and 300, as finer motion is needed for this task. We provide part of
the loop pattern in-context, and assess the ability to extrapolate from 2 loops to do a third loop. LLMs, e.g.,
text-davinci-003 perform well—we show more completions with different loop styles in the Appendix.
LargeLanguageModelsasGeneralPatternMachines