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.

本文介绍了一种自主学习的方法，通过无标注的高维真实值机器人轨迹，自动学习抽象状态和动作的可推广的基于逻辑的关系表示，形成了自动发明的类似 PDDL 的领域模型。通过确定性设置的实证结果表明，仅凭少量机器人轨迹就可以学习到强大的抽象表示，所学的关系表示包括但超越了高级动作的经典直观概念，并且学习的模型使得规划算法能够扩展到以前超出手工构思抽象的任务范围。