The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because actions usually have several possible results, and sensors are prone to produce measurements with error. Partially observable Markov decision processes (POMDPs) are commonly employed, thanks to their ability to model the uncertainty of actions that modify and monitor the state of a system. However, since solving a POMDP is computationally expensive, their usage becomes prohibitive for most robotic applications. In this article, we propose a task planning architecture for service robotics. In the context of service robot design, we present a scheme to encode knowledge about the robot and its environment, that promotes the modularity and reuse of information. Also, we introduce a new recursive definition of a POMDP that enables our architecture to autonomously build a hierarchy of POMDPs, so that it can be used to generate and execute plans that solve the task at hand. Experimental results show that, in comparison to baseline methods, by following a recursive hierarchical approach the architecture is able to significantly reduce the planning time, while maintaining (or even improving) the robustness under several scenarios that vary in uncertainty and size.

任务计划中的主要目标是建立一系列动作，以使代理从初始状态转变为目标状态。在机器人技术中，这特别困难，因为动作通常会产生几种可能的结果，并且传感器容易产生错误的测量结果。由于部分可观察的马尔可夫决策过程（POMDP）能够对修改和监视系统状态的动作的不确定性进行建模，因此通常被采用。但是，由于解决POMDP的计算量很大，因此对于大多数机器人应用来说，其使用变得越来越困难。在本文中，我们提出了一种服务机器人技术的任务计划架构。在服务机器人设计的上下文中，我们提出了一种对机器人及其周围环境的知识进行编码的方案，以促进信息的模块化和重用。还，我们引入了POMDP的新递归定义，该定义使我们的体系结构能够自动构建POMDP的层次结构，以便可用于生成和执行解决当前任务的计划。实验结果表明，与基线方法相比，通过遵循递归分层方法，该体系结构能够显着减少计划时间，同时在不确定性和规模各不相同的几种情况下保持（甚至提高）鲁棒性。