Leveraging Imitation Learning on Pose Regulation Problem of a Robotic Fish.

In this article, the pose regulation control problem of a robotic fish is investigated by formulating it as a Markov decision process (MDP). Such a typical task that requires the robot to arrive at the desired position with the desired orientation remains a challenge, since two objectives (position and orientation) may be conflicted during optimization. To handle the challenge, we adopt the sparse reward scheme, i.