Autonomously exploring the unknown physical properties of novel objects such as stiffness, mass, center of mass, friction coefficient, and shape is crucial for autonomous robotic systems operating continuously in unstructured environments. We introduce a novel visuo-tactile based predictive cross-modal perception framework where initial visual observations (shape) aid in obtaining an initial prior over the object properties (mass). The initial prior improves the efficiency of the object property estimation, which is autonomously inferred via interactive non-prehensile pushing and using a dual filtering approach. The inferred properties are then used to enhance the predictive capability of the cross-modal function efficiently by using a human-inspired `surprise' formulation. We evaluated our proposed framework in the real-robotic scenario, demonstrating superior performance.

翻译：自主探索未知物体的物理属性（如硬度、质量、质心、摩擦系数和形状）对于在非结构化环境中持续运行的自主机器人系统至关重要。本文提出一种新颖的基于视觉触觉的预测性跨模态感知框架，其中初始视觉观测（形状）有助于获取物体属性（质量）的初始先验分布。该初始先验提升了物体属性估计的效率，这些属性通过交互式非抓取推动操作并采用双重滤波方法自主推断得出。随后，我们借鉴人类认知中的"意外度"计算模型，利用推断出的属性有效增强跨模态函数的预测能力。我们在真实机器人场景中对所提框架进行了评估，结果证明了其优越性能。