Creating agile and responsive characters from a collection of unorganized human motion has been an important problem of constructing interactive virtual environments. Recently, learning-based approaches have successfully been exploited to learn deep network policies for the control of interactive characters. The agility and responsiveness of deep network policies are influenced by many factors, such as the composition of training datasets, the architecture of network models, and learning algorithms that involve many threshold values, weights, and hyper-parameters. In this paper, we present a novel teacher-student framework to learn time-critically responsive policies, which guarantee the time-to-completion between user inputs and their associated responses regardless of the size and composition of the motion databases. We demonstrate the effectiveness of our approach with interactive characters that can respond to the user's control quickly while performing agile, highly dynamic movements.

We present a novel and general framework for the design and control of underwater soft-bodied animals. The whole body of an animal consisting of soft tissues is modeled by tetrahedral and triangular FEM meshes. The contraction of muscles embedded in the soft tissues actuates the body and limbs to move. We present a novel muscle excitation model that mimics the anatomy of muscular hydrostats and their muscle excitation patterns. Our deep reinforcement learning algorithm equipped with the muscle excitation model successfully learned the control policy of soft-bodied animals, which can be physically simulated in real-time, controlled interactively, and resilient to external perturbations. We demonstrate the effectiveness of our approach with various simulated animals including octopuses, lampreys, starfishes, stingrays and cuttlefishes. They learn diverse behaviors such as swimming, grasping, and escaping from a bottle. We also implemented a simple user interface system that allows the user to easily create their creatures.