Automated Learning of Muscle- Actuated Locomotion Through Control Abstraction Radek Grzeszczuk and Demetri Terzopoulos Presented by Johann Hukari

Animating animals is hard Muscle interactions are complex, even in “lower” life forms People notice when animals walk like they have a stick up their ass Is it possible for a physics based simulation to control itself?

Real animals are efficient locomotors Locomotion is a learned skill Behaviors are constructed from lower level behaviors and combined into more complex actions

This paper explores extremely flexible bodies: Things that slither or swim Bodies constructed from spring-mass systems, many DOFs Providing animators with controls to every muscle (even simplified versions) is needlessly complex.

Instead of direct control, let the animal control itself Generate and test. Did this change result in better motion? Low level motions combined to generate more complex motion.

Learning low-level control can be lengthy, but is fairly simple “Muscles” are control according to a scalar activation function

“Animals” are allowed time to learn simple locomotion and turns of various radii These actions are combined together with approx 5% which is blended to disguise discontinuities When given goals “animals” choose the locally optimal solution: “greedy”

Stupid pet tricks Dolphin can learn “Sea World” style tricks by combining swimming and turning Dolphin tail modeled by turning previously generated shark’s tail sideways.

Simply sequencing controllers greedily is simplistic Other method: Learn complex behaviors by combining (rather than sequencing) Doesn’t use intermediate solutions. Complex combinatorial problem