  We study the problem of controllable generation of long-term sequential
behaviors, where the goal is to calibrate to multiple behavior styles
simultaneously. In contrast to the well-studied areas of controllable
generation of images, text, and speech, there are two questions that pose
significant challenges when generating long-term behaviors: how should we
specify the factors of variation to control, and how can we ensure that the
generated behavior faithfully demonstrates combinatorially many styles? We
leverage programmatic labeling functions to specify controllable styles, and
derive a formal notion of style-consistency as a learning objective, which can
then be solved using conventional policy learning approaches. We evaluate our
framework using demonstrations from professional basketball players and agents
in the MuJoCo physics environment, and show that existing approaches that do
not explicitly enforce style-consistency fail to generate diverse behaviors
whereas our learned policies can be calibrated for up to 1024 distinct style
combinations.
