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The robotic manipulation of compliant objects is currently one of the mostactive problems in robotics due to its potential to automate many importantapplications. Despite the progress achieved by the robotics community in recentyears, the 3D shaping of these types of materials remains an open researchproblem. In this paper, we propose a new vision-based controller toautomatically regulate the shape of compliant objects with robotic arms. Ourmethod uses an efficient online surface/curve fitting algorithm that quantifiesthe object's geometry with a compact vector of features; This feedback-likevector enables to establish an explicit shape servo-loop. To coordinate themotion of the robot with the computed shape features, we propose areceding-time estimator that approximates the system's sensorimotor model whilesatisfying various performance criteria. A deep adversarial network isdeveloped to robustly compensate for visual occlusions in the camera's field ofview, which enables to guide the shaping task even with partial observations ofthe object. Model predictive control is utilized to compute the robot's shapingmotions subject to workspace and saturation constraints. A detailedexperimental study is presented to validate the effectiveness of the proposedcontrol framework.

Model Predictive Manipulation of Compliant Objects with Multi-Objective Optimizer and Adversarial Network for Occlusion Compensation