  Motion synthesis in a dynamic environment has been a long-standing problem
for character animation. Methods using motion capture data tend to scale poorly
in complex environments because of their larger capturing and labeling
requirement. Physics-based controllers are effective in this regard, albeit
less controllable. In this paper, we present CARL, a quadruped agent that can
be controlled with high-level directives and react naturally to dynamic
environments. Starting with an agent that can imitate individual animation
clips, we use Generative Adversarial Networks to adapt high-level controls,
such as speed and heading, to action distributions that correspond to the
original animations. Further fine-tuning through the deep reinforcement
learning enables the agent to recover from unseen external perturbations while
producing smooth transitions. It then becomes straightforward to create
autonomous agents in dynamic environments by adding navigation modules over the
entire process. We evaluate our approach by measuring the agent's ability to
follow user control and provide a visual analysis of the generated motion to
show its effectiveness.
