  In computer animation, driving a simulated character with lifelike motion is
challenging. Current generative models, though able to generalize to diverse
motions, often pose challenges to the responsiveness of end-user control. To
address these issues, we introduce RACon: Retrieval-Augmented Simulated
Character Locomotion Control. Our end-to-end hierarchical reinforcement
learning method utilizes a retriever and a motion controller. The retriever
searches motion experts from a user-specified database in a task-oriented
fashion, which boosts the responsiveness to the user's control. The selected
motion experts and the manipulation signal are then transferred to the
controller to drive the simulated character. In addition, a retrieval-augmented
discriminator is designed to stabilize the training process. Our method
surpasses existing techniques in both quality and quantity in locomotion
control, as demonstrated in our empirical study. Moreover, by switching
extensive databases for retrieval, it can adapt to distinctive motion types at
run time.
