Interactive exploration of the unknown physical properties of objects such as stiffness, mass, center of mass, friction coefficient, and shape is crucial for autonomous robotic systems operating continuously in unstructured environments. Precise identification of these properties is essential to manipulate objects in a stable and controlled way, and is also required to anticipate the outcomes of (prehensile or non-prehensile) manipulation actions such as pushing, pulling, lifting, etc. Our study focuses on autonomously inferring the physical properties of a diverse set of various homogeneous, heterogeneous, and articulated objects utilizing a robotic system equipped with vision and tactile sensors. We propose a novel predictive perception framework for identifying object properties of the diverse objects by leveraging versatile exploratory actions: non-prehensile pushing and prehensile pulling. As part of the framework, we propose a novel active shape perception to seamlessly initiate exploration. Our innovative dual differentiable filtering with Graph Neural Networks learns the object-robot interaction and performs consistent inference of indirectly observable time-invariant object properties. In addition, we formulate a $N$-step information gain approach to actively select the most informative actions for efficient learning and inference. Extensive real-robot experiments with planar objects show that our predictive perception framework results in better performance than the state-of-the-art baseline and demonstrate our framework in three major applications for i) object tracking, ii) goal-driven task, and iii) change in environment detection.

翻译：在非结构化环境中持续运行的自主机器人系统，对物体未知物理属性（如刚度、质量、质心、摩擦系数和形状）的交互式探索至关重要。精确识别这些属性对于以稳定可控的方式操纵物体必不可少，同时也是预测推、拉、举等（抓取或非抓取）操作结果的前提。本研究聚焦于利用配备视觉与触觉传感器的机器人系统，自主推断多种均质、非均质及关节式物体的物理属性。我们提出了一种新颖的预测性感知框架，通过利用多样化的探索动作——非抓取式推动与抓取式拉动——来识别各类物体的属性。作为框架的组成部分，我们提出了一种创新的主动形状感知方法以无缝启动探索过程。我们创新的双可微滤波结合图神经网络，能够学习物体与机器人的交互作用，并对间接可观测的时不变物体属性进行一致性推断。此外，我们构建了一种$N$步信息增益方法，以主动选择信息量最大的动作，从而实现高效学习与推断。大量平面物体真实机器人实验表明，我们的预测性感知框架性能优于当前最先进的基线方法，并在三大应用场景中验证了其有效性：i) 物体跟踪，ii) 目标驱动任务，以及 iii) 环境变化检测。