Accurate Robotic Pushing Manipulation Through Online Model Estimation Under Uncertain Object Properties

Robotic pushing is a fundamental non-prehensile manipulation skill essential for handling objects that are difficult to grasp. This letter proposes a highly accurate robotic pushing framework that utilizes an online estimated model to push objects along a given nominal trajectory, despite uncertain object properties such as friction coefficients, mass distribution, and the position of the center of friction (CoF). The core concept involves estimating an optimal pushing motion model capable of representing observed local motions. A generalized form of the conventional analytical model, coupled with a moving-window Unscented Kalman Filter (UKF), serves as the online estimated model. It captures the local behavior of the pushed objects and is integrated with a model predictive control-based pushing strategy to achieve precise pushing performance. In experiments, the proposed robotic pushing framework demonstrated superior accuracy in tracking the given nominal trajectory compared to the conventional analytical model and data-driven model approaches, even when the motion model was perturbed. Additionally, the practicality of the proposed framework was showcased through a demonstration involving an autonomous robot collecting dishes, illustrating its applicability in various real-world applications.