Low Dimensional Motor Skill Learning Using Coactivation
We propose an approach for motor skill learning of highly articulated characters based on the systematic exploration of low-dimensional
joint coactivation spaces. Through analyzing human motion, we first show that the dimensionality of many motion tasks is much smaller than
the full degrees of freedom (DOFs) of the character. Indeed, joint motion appears organized across DOFs, with multiple joints moving together
and working in synchrony. We exploit such redundancy for character control by extracting task-specific joint coactivations from human recorded
motion, capturing synchronized patterns of simultaneous joint movements that effectively reduce the control space across DOFs. By learning how
to excite such coactivations using deep reinforcement learning, we are able to train humanlike controllers using only a small number of
dimensions. We demonstrate our approach on a range of motor tasks and show its flexibility against a variety of reward functions, from
minimalistic rewards that simply follow the center-of-mass of a reference trajectory to carefully shaped ones that fully track reference
characters. In all cases, by learning a 10-dimensional controller on a full 28 DOF character, we reproduce high-fidelity locomotion even
in the presence of sparse reward functions.
Highlights Video and Presentation
A. Ranganath P. Xu, I. Karamouzas, and V Zordan, “Low Dimensional Motor Skill Learning Using Coactivation”,
in Motion, Interaction and Games, 2019, Newcastle, UK, Oct 2019.