Hand-crafted, logic-based state and action representations have been widely used to overcome the intractable computational complexity of long-horizon robot planning problems, including task and motion planning problems. However, creating such representations requires experts with strong intuitions and detailed knowledge about the robot and the tasks it may need to accomplish in a given setting. Removing this dependency on human intuition is a highly active research area. This paper presents the first approach for autonomously learning generalizable, logic-based relational representations for abstract states and actions starting from unannotated high-dimensional, real-valued robot trajectories. The learned representations constitute auto-invented PDDL-like domain models. Empirical results in deterministic settings show that powerful abstract representations can be learned from just a handful of robot trajectories; the learned relational representations include but go beyond classical, intuitive notions of high-level actions; and that the learned models allow planning algorithms to scale to tasks that were previously beyond the scope of planning without hand-crafted abstractions.

翻译：手工构建的基于逻辑的状态和动作表示已被广泛用于克服长时域机器人规划问题（包括任务与运动规划问题）中难解的计算复杂性。然而，创建此类表示需要具备强大直觉和详细知识（关于机器人及其在特定环境中需完成的任务）的专家。消除这种对人类直觉的依赖是一个高度活跃的研究领域。本文提出了首种从无标注的高维实值机器人轨迹中自主学习可泛化的、基于逻辑的关系型抽象状态与动作表示的方法。学习到的表示构成了自动发明的类PDDL领域模型。在确定性环境中的实验结果表明：仅需少量机器人轨迹即可学习到强大的抽象表示；所学习的关表示不仅包含经典、直观的高层动作概念，更超越了这些概念；同时，所学习的模型使得规划算法能够扩展至此前因缺乏手工抽象而超出规划能力范围的任务。