  Sensory feedback is essential for the control of soft robotic systems and to
enable deployment in a variety of different tasks. Proprioception refers to
sensing the robot's own state and is of crucial importance in order to deploy
soft robotic systems outside of laboratory environments, i.e. where no external
sensing, such as motion capture systems, is available.
  A vision-based sensing approach for a soft robotic arm made from fabric is
presented, leveraging the high-resolution sensory feedback provided by cameras.
No mechanical interaction between the sensor and the soft structure is required
and consequently, the compliance of the soft system is preserved. The
integration of a camera into an inflatable, fabric-based bellow actuator is
discussed. Three actuators, each featuring an integrated camera, are used to
control the spherical robotic arm and simultaneously provide sensory feedback
of the two rotational degrees of freedom. A convolutional neural network
architecture predicts the two angles describing the robot's orientation from
the camera images. Ground truth data is provided by a motion capture system
during the training phase of the supervised learning approach and its
evaluation thereafter.
  The camera-based sensing approach is able to provide estimates of the
orientation in real-time with an accuracy of about one degree. The reliability
of the sensing approach is demonstrated by using the sensory feedback to
control the orientation of the robotic arm in closed-loop.
