Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo Code: https://git.io/JCT0g
翻译:混合状态和行动空间的机器人规划问题可以通过综合任务和运动规划者(TAMP)来解决,后者处理运动一级决定和任务一级计划可行性之间的复杂互动。TAMP方法依靠特定领域的象征性操作者来指导任务一级的搜索,使规划效率得到提高。在这项工作中,我们正式确定并研究TAMP操作者学习的问题。本研究的核心是认为操作者定义一个域过渡模式的失失失抽象抽象。然后我们提出一个自下而上的操作者学习关系学习方法,并表明如何在TAMP系统中利用学习者进行规划。我们实验性地提供三个领域的成果,包括长视距机器人规划任务。我们发现我们大大超越若干基线的方法,包括最近文献中的三个基于图形网络的无型样方法。视频:https://yout.be/iVfpX9BRo code:https://git.io/JCT0g。