  Controlling robotic manipulators with high-dimensional action spaces for
dexterous tasks is a challenging problem. Inspired by human manipulation,
researchers have studied generating and using postural synergies for robot
hands to accomplish manipulation tasks, leveraging the lower dimensional nature
of synergistic action spaces. However, many of these works require
pre-collected data from an existing controller in order to derive such a
subspace by means of dimensionality reduction. In this paper, we present a
framework that simultaneously discovers a synergy space and a multi-task policy
that operates on this low-dimensional action space to accomplish diverse
manipulation tasks. We demonstrate that our end-to-end method is able to
perform multiple tasks using few synergies, and outperforms sequential methods
that apply dimensionality reduction to independently collected data. We also
show that deriving synergies using multiple tasks can lead to a subspace that
enables robots to efficiently learn new manipulation tasks and interactions
with new objects.
