There has been increasing attention on planning model learning in classical
planning. Most existing approaches, however, focus on learning planning models
from structured data in symbolic representations. It is often difficult to
obtain such structured data in real-world scenarios. Although a number of
approaches have been developed for learning planning models from fully observed
unstructured data (e.g., images), in many scenarios raw observations are often
incomplete. In this paper, we provide a novel framework, \aType{Recplan}, for
learning a transition model from partially observed raw image traces. More
specifically, by considering the preceding and subsequent images in a trace, we
learn the latent state representations of raw observations and then build a
transition model based on such representations. Additionally, we propose a
neural-network-based approach to learn a heuristic model that estimates the
distance toward a given goal observation. Based on the learned transition model
and heuristic model, we implement a classical planner for images. We exhibit
empirically that our approach is more effective than a state-of-the-art
approach of learning visual planning models in the environment with incomplete
observations.

本文提供了一种新框架 Recplan，用于从部分观察到的原始图像轨迹中学习转换模型。通过考虑轨迹中的前面和后面的图像，我们学习原始观察的潜在状态表示，然后基于这样的表示构建转换模型。此外，我们提出了一种基于神经网络的方法，学习估计朝着给定目标观察的距离的启发式模型。基于学习的转换模型和启发式模型，我们实现了一个图像的经典规划器，并在实证中展示了我们的方法比在具有不完整观测的环境中学习视觉规划模型的最新方法更有效。