We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN). Consisting of a perception module that extracts representations of latent object properties and a prediction module that uses those extracted properties to simulate system dynamics, the PPN can be trained in an end-to-end fashion purely from samples of object dynamics. The representations of latent object properties learned by PPNs not only are sufficient to accurately simulate the dynamics of systems comprised of previously unseen objects, but also can be translated directly into human-interpretable properties (e.g., mass, coefficient of restitution) in an entirely unsupervised manner. Crucially, PPNs also generalize to novel scenarios: their gradient-based training can be applied to many dynamical systems and their graph-based structure functions over systems comprised of different numbers of objects. Our results demonstrate the efficacy of graph-based neural architectures in object-centric inference and prediction tasks, and our model has the potential to discover relevant object properties in systems that are not yet well understood.

提出了一种框架，用于完全无监督地学习潜在的物体属性: 感知-预测网络 (PPN)，该网络由感知模块和预测模块组成，可以纯粹从物体动态的样本中进行端到端的训练，学习的潜在物体属性可以被直接翻译成人类可解释的属性，此模型在物体中心推理和预测任务中具有很高的效能，且具有发现未被充分解释的系统中的相关对象属性的潜力

利用感知-预测网络无监督学习潜在物理属性