论文标题
谓语发明用于二重性计划
Predicate Invention for Bilevel Planning
论文作者
论文摘要
即使过渡模型是确定性和已知的，在连续状态和行动空间中的有效计划也很难。缓解这一挑战的一种方法是用抽象进行二重性计划，其中使用高级搜索抽象计划来指导原始过渡空间中的计划。先前的工作表明，当用符号谓词的状态抽象被手工设计时，可以从演示中学到用于双重计划的操作员和采样器。在这项工作中，我们提出了一种从演示中学习谓词的算法，消除了对手动指定的状态抽象的需求。我们的关键思想是通过优化替代目标，但忠实于我们真正的有效计划目标的替代目标来学习谓词。我们在对语法绘制的谓词集的爬山搜索中使用了这个替代目标。在实验上，我们在四个机器人计划环境中显示，我们所学的抽象能够快速解决持有的任务，表现优于六个基准。代码：https：//tinyurl.com/predicators-release
Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. Previous work has shown that when state abstractions in the form of symbolic predicates are hand-designed, operators and samplers for bilevel planning can be learned from demonstrations. In this work, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective. We use this surrogate objective in a hill-climbing search over predicate sets drawn from a grammar. Experimentally, we show across four robotic planning environments that our learned abstractions are able to quickly solve held-out tasks, outperforming six baselines. Code: https://tinyurl.com/predicators-release
