条件时序偏序：一种用于机器人任务规范与规划的表达力强且可解释的框架
Conditional Timed Partial Orders: An Expressive and Interpretable Framework for Robot Task Specification and Planning
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中文总结 AI 辅助
本文提出条件时序偏序（cTPO）框架，扩展TPO以支持更丰富的时序约束和条件事件激活，并通过完备的分解算法将规划问题拆分为多个小MILP，实现高达四个数量级的加速，兼顾表达力与可解释性。
中文摘要 AI 辅助
时序偏序（Timed Partial Orders, TPOs）最初为工作流提出，为机器人任务规范提供了一个可解释的框架，并配有基于混合整数线性规划（MILP）的规划算法。然而，TPOs在表达力上受限，仅能捕获带有简单时序约束的偏序事件。在本文中，我们引入了条件时序偏序（Conditional TPOs, cTPOs），它通过更丰富的相对时序约束和基于环境条件的条件事件激活来扩展TPOs。我们证明了cTPOs的规划问题同样可归结为MILP问题；然而，增加的表达力导致MILP规模显著增大，可能变得计算上难以处理。为应对这一挑战，我们提出了一种分解算法，将cTPO划分为更小的子TPO，从而产生一系列更小的MILP问题。我们证明了该分解是完备的，且能保持规划最优性，同时提高复杂任务的可解释性。实验结果展示了cTPOs作为任务规范框架的有效性以及我们分解方法的效率，相比整体式MILP实现了高达四个数量级的加速。
英文摘要
Timed Partial Orders (TPOs), originally proposed for workflows, provide an interpretable framework for robot task specification with planning algorithms based on mixed-integer linear programming (MILP). However, TPOs are limited in expressivity, capturing only partial-order events with simple timing constraints. In this paper, we introduce Conditional TPOs (cTPOs), which extend TPOs with richer relative-timing constraints and conditional event activations based on environmental conditions. We show that planning for cTPOs also reduces to an MILP problem; however, the added expressivity results in significantly larger MILPs that can become computationally intractable. To address this challenge, we propose a decomposition algorithm that partitions a cTPO into smaller sub-TPOs, yielding a sequence of smaller MILP problems. We prove that this decomposition is complete and preserves plan optimality while improving the interpretability of complex tasks. Experimental results demonstrate the effectiveness of cTPOs as a task specification framework and the efficiency of our decomposition approach, achieving up to four orders of magnitude speedup over the monolithic MILP.
发表机构
- University of Colorado Boulder（科罗拉多大学博尔德分校）
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