Object-based approaches for learning action-conditioned dynamics has
demonstrated promise for generalization and interpretability. However, existing
approaches suffer from structural limitations and optimization difficulties for
common environments with multiple dynamic objects. In this paper, we present a
novel self-supervised learning framework, called Multi-level Abstraction
Object-oriented Predictor (MAOP), which employs a three-level learning
architecture that enables efficient object-based dynamics learning from raw
visual observations. We also design a spatial-temporal relational reasoning
mechanism for MAOP to support instance-level dynamics learning and handle
partial observability. Our results show that MAOP significantly outperforms
previous methods in terms of sample efficiency and generalization over novel
environments for learning environment models. We also demonstrate that learned
dynamics models enable efficient planning in unseen environments, comparable to
true environment models. In addition, MAOP learns semantically and visually
interpretable disentangled representations.

本研究提出了一个多层次抽象的物体导向预测器 (MAOP) 的自我监督学习框架，该框架通过三级学习结构实现了从生视觉观察中有效的基于物体的动态学习，并设计了一种空间 - 时间关系推理机制来支持实例级的动态学习和处理部分可见性。结果表明，与以往的方法相比，MAOP 在样本效率和学习环境模型的新环境通用性方面显著优于以前的方法。此外，MAOP 还能够学习具有语义和可视可解释性的去耦合表示，并能在未知环境中实现高效规划。