  Robots built from soft materials can alter their shape and size in a
particular profile. This shape-changing ability could be extremely helpful for
rescue robots and those operating in unknown terrains and environments. In
changing shape, soft materials also store and release elastic energy, a feature
that can be exploited for effective robot movement. However, design and control
of these moving soft robots are non-trivial. The research presents design
methodology for a 3D-printed, motor-tendon actuated soft robot capable of
locomotion. In addition to shape change, the robot uses friction manipulation
mechanisms to effect locomotion. The motor-tendon actuators comprise of nylon
tendons embedded inside the soft body structure along a given path with one end
fixed on the body and the other attached to a motor. These actuators directly
control the deformation of the soft body which influences the robot locomotion
behavior. Static stress analysis is used as a tool for designing the shape of
the paths of these tendons embedded inside the body. The research also presents
a novel model-free learning-based control approach for soft robots which
interact with the environment at discrete contact points. This approach
involves discretization of factors dominating robot-environment interactions as
states, learning of the results as robot transitions between these robot states
and evaluation of desired periodic state control sequences optimizing a cost
function corresponding to a locomotion task (rotation or translation). The
clever discretization allows the framework to exist in robot's task space,
hence, facilitating calculation of control sequences without modeling the
actuator, body material or details of the friction mechanisms. The flexibility
of the framework is experimentally explored by applying it to robots with
different friction mechanisms and different shapes of tendon paths.
