  Bionic underwater robots have demonstrated their superiority in many
applications. Yet, training their intelligence for a variety of tasks that
mimic the behavior of underwater creatures poses a number of challenges in
practice, mainly due to lack of a large amount of available training data as
well as the high cost in real physical environment. Alternatively, simulation
has been considered as a viable and important tool for acquiring datasets in
different environments, but it mostly targeted rigid and soft body systems.
There is currently dearth of work for more complex fluid systems interacting
with immersed solids that can be efficiently and accurately simulated for robot
training purposes. In this paper, we propose a new platform called "FishGym",
which can be used to train fish-like underwater robots. The framework consists
of a robotic fish modeling module using articulated body with skinning, a
GPU-based high-performance localized two-way coupled fluid-structure
interaction simulation module that handles both finite and infinitely large
domains, as well as a reinforcement learning module. We leveraged existing
training methods with adaptations to underwater fish-like robots and obtained
learned control policies for multiple benchmark tasks. The training results are
demonstrated with reasonable motion trajectories, with comparisons and analyses
to empirical models as well as known real fish swimming behaviors to highlight
the advantages of the proposed platform.
