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与动作表征长期以来被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，创建此类表征需要专家对机器人在特定场景中需完成的任务具备深刻直觉与详细知识。消除这种对人类直觉的依赖是一个高度活跃的研究领域。本文提出首个方法，能够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征。所学习的表征构成了自动生成的类PDDL域模型。确定性环境中的实验结果表明：仅凭少量机器人轨迹即可习得强大的抽象表征；习得的关系型表征涵盖并超越了经典直观的高层动作概念；所生成的模型使得规划算法能够扩展到此前因缺乏手工抽象而无法规划的复杂任务。