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：基于人工设计的逻辑状态与动作表示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）中棘手的计算复杂性，然而，创建此类表示需要专家具备关于机器人在给定场景中可能需完成任务的强烈直觉与详细知识。消除这种对人类直觉的依赖是一个高度活跃的研究领域。本文提出了首个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。所学习的表示构成了自动生成的类PDDL领域模型。确定性环境下的实证结果表明：仅需少量机器人轨迹即可学习到强大的抽象表示；学习到的关系型表示包含但超越了经典、直观的高层动作概念；且所学习的模型能使得规划算法扩展到此前无手工抽象时难以企及的任务规模。