UNO Push: Unified Nonprehensile Object Pushing via Non-Parametric Estimation and Model Predictive Control
CoRR(2024)
摘要
Nonprehensile manipulation through precise pushing is an essential skill that
has been commonly challenged by perception and physical uncertainties, such as
those associated with contacts, object geometries, and physical properties. For
this, we propose a unified framework that jointly addresses system modeling,
action generation, and control. While most existing approaches either heavily
rely on a priori system information for analytic modeling, or leverage a large
dataset to learn dynamic models, our framework approximates a system transition
function via non-parametric learning only using a small number of exploratory
actions (ca. 10). The approximated function is then integrated with model
predictive control to provide precise pushing manipulation. Furthermore, we
show that the approximated system transition functions can be robustly
transferred across novel objects while being online updated to continuously
improve the manipulation accuracy. Through extensive experiments on a real
robot platform with a set of novel objects and comparing against a
state-of-the-art baseline, we show that the proposed unified framework is a
light-weight and highly effective approach to enable precise pushing
manipulation all by itself. Our evaluation results illustrate that the system
can robustly ensure millimeter-level precision and can straightforwardly work
on any novel object.
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