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

TL;DR

This paper introduces a vision-based control framework for robotic shaping of compliant objects, integrating surface fitting, a receding estimator, adversarial occlusion compensation, and model predictive control for robust, constrained manipulation.

Contribution

It presents a novel integrated approach combining shape sensing, occlusion handling, and predictive control for compliant object manipulation.

Findings

Effective shape regulation demonstrated in experiments.

Robust occlusion compensation via adversarial network.

Improved manipulation accuracy under constraints.

Abstract

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.