We present an end-to-end framework for realizing
fully automated gait learning for a complex underwater legged
robot. Using this framework, we demonstrate that a hexapod
flipper-propelled robot can learn task-specific control policies
We present an end-to-end framework for realizing
fully automated gait learning for a complex underwater legged
robot. Using this framework, we demonstrate that a hexapod
flipper-propelled robot can learn task-specific control policies
purely from experience data. Our method couples a state-of-theart
policy search technique with a family of periodic low-level
controls that are well suited for underwater propulsion. We
demonstrate the practical efficacy of tabula rasa learning, that
is, learning without the use of any prior knowledge, of policies
for a six-legged swimmer to carry out a variety of acrobatic
maneuvers in three dimensional space. We also demonstrate
informed learning that relies on simulated experience from
a realistic simulator. In numerous cases, novel emergent gait
behaviors have arisen from learning, such as the use of one
stationary flipper to create drag while another oscillates to
create thrust. Similar effective results have been demonstrated
in under-actuated configurations, where as few as two flippers
are used to maneuver the robot to a desired pose, or through
an acrobatic motion such as a corkscrew. The success of our
learning framework is assessed both in simulation and in the
field using an underwater swimming robot.