This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, containing over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations.

翻译：本文提出了一种框架，通过机器人操作和物体测量数据库自动提取物理属性（如材料成分、质量、体积和刚度）。该框架采用探索性动作选择策略，以最大化对桌面上物体的学习效果。贝叶斯网络对物体属性间的条件依赖关系进行建模，并结合了先验概率分布及测量动作相关的不确定性。该算法基于期望信息增益选择最优探索动作，并通过贝叶斯推断更新物体属性。实验评估表明，与基线方法相比，该方法能有效选择动作，并在无新知识可习得时正确终止实验。当面对材料属性与外观相矛盾的迷惑性物体时，该算法表现出智能行为。机器人流水线集成了日志模块和在线物体数据库，该数据库包含使用不同夹爪对63个物体进行的超过24,000次测量。所有代码和数据均已公开，可通过探索性操作实现物体及其物理属性的自动化数字化。