  Legged robots need to be capable of walking on diverse terrain conditions. In
this paper, we present a novel reinforcement learning framework for learning
locomotion on non-rigid dynamic terrains. Specifically, our framework can
generate quadruped locomotion on flat elastic terrain that consists of a matrix
of tiles moving up and down passively when pushed by the robot's feet. A
trained robot with 55cm base length can walk on terrain that can sink up to
5cm. We propose a set of observation and reward terms that enable this
locomotion; in which we found that it is crucial to include the end-effector
history and end-effector velocity terms into observation. We show the
effectiveness of our method by training the robot with various terrain
conditions.
