  We present a methodology for fast prototyping of morphologies and controllers
for robot locomotion. Going beyond simulation-based approaches, we argue that
the form and function of a robot, as well as their interplay with real-world
environmental conditions are critical. Hence, fast design and learning cycles
are necessary to adapt robot shape and behavior to their environment. To this
end, we present a combination of laminate robot manufacturing and
sample-efficient reinforcement learning. We leverage this methodology to
conduct an extensive robot learning experiment. Inspired by locomotion in sea
turtles, we design a low-cost crawling robot with variable, interchangeable
fins. Learning is performed using both bio-inspired and original fin designs in
an artificial indoor environment as well as a natural environment in the
Arizona desert. The findings of this study show that static policies developed
in the laboratory do not translate to effective locomotion strategies in
natural environments. In contrast to that, sample-efficient reinforcement
learning can help to rapidly accommodate changes in the environment or the
robot.
