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.

翻译：通过精确推动实现非抓取式操作是一项关键技能，但常受感知与物理不确定性（如接触、物体几何形状及物理属性相关的不确定性）的挑战。为此，我们提出一个统一框架，联合解决系统建模、动作生成与控制问题。现有方法大多严重依赖先验系统信息进行解析建模，或利用大规模数据集学习动力学模型，而我们的框架仅需少量探索性动作（约10次）即可通过非参数学习逼近系统转移函数。随后将逼近函数与模型预测控制相结合，实现精确推动操作。进一步研究表明，所逼近的系统转移函数可稳健地迁移至未知物体，并通过在线更新持续提升操作精度。在真实机器人平台上对一组未知物体进行的广泛实验及与当前最优基线的对比表明，所提统一框架是一种轻量级且高效的方法，能够独立实现精确推动操作。评估结果表明，该系统可稳健地达到毫米级精度，并可直接适用于任意未知物体。