  This paper presents the application of a learning control approach for the
realization of a fast and reliable pick-and-place application with a spherical
soft robotic arm. The arm is characterized by a lightweight design and exhibits
compliant behavior due to the soft materials deployed. A soft, continuum joint
is employed, which allows for simultaneous control of one translational and two
rotational degrees of freedom in a single joint. This allows us to axially
approach and pick an object with the attached suction cup during the
pick-and-place application. A control allocation based on pressure differences
and the antagonistic actuator configuration is introduced, allowing decoupling
of the system dynamics and simplifying the modeling and control. A linear
parameter-varying model is identified, which is parametrized by the attached
load mass and a parameter related to the joint stiffness. A gain-scheduled
feedback controller is proposed, which asymptotically stabilizes the robotic
system for aggressive tuning and over large variations of the parameters
considered. The control architecture is augmented with an iterative learning
control scheme enabling accurate tracking of aggressive trajectories involving
set point transitions of 60 degrees within 0.3 seconds (no mass attached) to
0.6 seconds (load mass attached). The modeling and control approach proposed
results in a reliable realization of a pick-and-place application and is
experimentally demonstrated.
