The exploding usage of physical object properties has greatly facilitated real-time applications such as robotics to perceive exactly as it appears in existence. Changes in the nature and properties of diverse real-time systems are associated with physical properties modification due to environmental factors. These physics-based object properties features attract the researchers’ attention while developing solutions to real-life problems. But, the detection and prediction of physical properties change are very diverse, covering many physics laws and object properties (material, shape, gravitational force, color, state change) which append complexity to these tasks. Instead of well-understood physics laws, elucidating physics laws requires substantial manual modeling with the help of standardized equations and associated factors. To adopt these physical laws to get instinctive and effective outcomes, researchers started applying computational models to learn changing property behavior as a substitute for using handcrafted and equipment-generated variable states. If physical properties detection challenges are not anticipated and required measures are not precluded, the upcoming computational model-driven physical object changing will not be able to serve appropriately. Therefore, this survey paper is drafted to demonstrate comprehensive theoretical and empirical studies of physical object properties detection and prediction. Furthermore, a generic paradigm is proposed to work in this direction along with characterization parameters of numerous physical object properties. A brief summarization of applicable machine learning, deep learning, and metaheuristic approaches is presented. An extensive list of released and openly available datasets for varying objects and parameters rendered for researchers. Additionally, performance measures regarding computational techniques for physical properties discovery and detection for quantitative evaluation of outcomes are also entailed. Finally, a few open research issues that need to be explored in the future are specified.

中文翻译：

---

用于物理对象属性发现、检测和预测的智能计算技术：综合调查

物理对象属性的爆炸式使用极大地促进了机器人等实时应用程序准确地感知其存在的情况。各种实时系统的性质和特性的变化与环境因素导致的物理特性修改相关。这些基于物理的对象属性特征在开发现实生活问题的解决方案时吸引了研究人员的注意力。但是，物理属性变化的检测和预测非常多样化，涵盖许多物理定律和物体属性（材料、形状、重力、颜色、状态变化），这增加了这些任务的复杂性。阐明物理定律需要借助标准化方程和相关因素进行大量手动建模，而不是充分理解的物理定律。为了采用这些物理定律来获得本能和有效的结果，研究人员开始应用计算模型来学习不断变化的财产行为，以代替使用手工制作和设备生成的可变状态。如果没有预见到物理属性检测的挑战并且没有排除所需的措施，即将到来的计算模型驱动的物理对象变化将无法适当地发挥作用。因此，起草这份调查报告是为了展示物理对象属性检测和预测的全面理论和实证研究。此外，还提出了一个通用范例，与许多物理对象属性的表征参数一起在这个方向上工作。简要总结了适用的机器学习、深度学习和元启发式方法。为研究人员提供的不同对象和参数的已发布和公开可用的数据集的广泛列表。此外，还需要有关用于物理特性发现和检测以定量评估结果的计算技术的性能测量。最后，指出了未来需要探索的一些开放性研究问题。