  The robotic manipulation of compliant objects is currently one of the most
active problems in robotics due to its potential to automate many important
applications. Despite the progress achieved by the robotics community in recent
years, the 3D shaping of these types of materials remains an open research
problem. In this paper, we propose a new vision-based controller to
automatically regulate the shape of compliant objects with robotic arms. Our
method uses an efficient online surface/curve fitting algorithm that quantifies
the object's geometry with a compact vector of features; This feedback-like
vector enables to establish an explicit shape servo-loop. To coordinate the
motion of the robot with the computed shape features, we propose a
receding-time estimator that approximates the system's sensorimotor model while
satisfying various performance criteria. A deep adversarial network is
developed to robustly compensate for visual occlusions in the camera's field of
view, which enables to guide the shaping task even with partial observations of
the object. Model predictive control is utilized to compute the robot's shaping
motions subject to workspace and saturation constraints. A detailed
experimental study is presented to validate the effectiveness of the proposed
control framework.
