  For legged robots to match the athletic capabilities of humans and animals,
they must not only produce robust periodic walking and running, but also
seamlessly switch between nominal locomotion gaits and more specialized
transient maneuvers. Despite recent advancements in controls of bipedal robots,
there has been little focus on producing highly dynamic behaviors. Recent work
utilizing reinforcement learning to produce policies for control of legged
robots have demonstrated success in producing robust walking behaviors.
However, these learned policies have difficulty expressing a multitude of
different behaviors on a single network. Inspired by conventional
optimization-based control techniques for legged robots, this work applies a
recurrent policy to execute four-step, 90 degree turns trained using reference
data generated from optimized single rigid body model trajectories. We present
a novel training framework using epilogue terminal rewards for learning
specific behaviors from pre-computed trajectory data and demonstrate a
successful transfer to hardware on the bipedal robot Cassie.
