For robotic systems to interact with objects in dynamic environments, it is essential to perceive the physical properties of the objects such as shape, friction coefficient, mass, center of mass, and inertia. This not only eases selecting manipulation action but also ensures the task is performed as desired. However, estimating the physical properties of especially novel objects is a challenging problem, using either vision or tactile sensing. In this work, we propose a novel framework to estimate key object parameters using non-prehensile manipulation using vision and tactile sensing. Our proposed active dual differentiable filtering (ADDF) approach as part of our framework learns the object-robot interaction during non-prehensile object push to infer the object's parameters. Our proposed method enables the robotic system to employ vision and tactile information to interactively explore a novel object via non-prehensile object push. The novel proposed N-step active formulation within the differentiable filtering facilitates efficient learning of the object-robot interaction model and during inference by selecting the next best exploratory push actions (where to push? and how to push?). We extensively evaluated our framework in simulation and real-robotic scenarios, yielding superior performance to the state-of-the-art baseline.

翻译：为使机器人系统在动态环境中与物体交互，必须感知物体的物理属性，如形状、摩擦系数、质量、质心及惯性。这不仅有助于选择合适的操控动作，还能确保任务按预期完成。然而，仅依赖视觉或触觉感知，估计尤其陌生物体的物理属性是一项具有挑战性的问题。本文提出一种新颖框架，通过非抓取操控结合视觉与触觉传感，估计关键物体参数。该框架中的主动双可微滤波（ADDF）方法，在非抓取物体推动过程中学习物体-机器人交互，以推断物体参数。所提方法使机器人系统能够利用视觉和触觉信息，通过非抓取物体推动主动探索陌生物体。所提出的新颖N步主动公式化方法内嵌于可微滤波中，通过选择下一个最佳探索性推动动作（推何处？如何推？），促进物体-机器人交互模型的高效学习与推理。我们在仿真和真实机器人场景中广泛评估了该框架，其性能优于现有最先进基线方法。