Object manipulation planning in clutter suffers from perception uncertainties due to occlusion, as well as action constraints required by collision avoidance. Partially observable Markov decision process (POMDP) provides a general model for planning under uncertainties. But a manipulation task usually have a large action space, which not only makes task planning intractable but also brings significant motion planning effort to check action feasibility. In this work, a new kind of hierarchical POMDP is presented for object manipulation tasks, in which a brief abstract POMDP is extracted and utilized together with the original POMDP. And a hierarchical belief tree search algorithm is proposed for efficient online planning, which constructs fewer belief nodes by building part of the tree with the abstract POMDP and invokes motion planning fewer times by determining action feasibility with observation function of the abstract POMDP. A learning mechanism is also designed in case there are unknown probabilities in transition and observation functions. This planning framework is demonstrated with an object fetching task and the performance is empirically validated by simulations and experiments.

杂波中的对象操作规划会因遮挡而导致感知不确定性，以及避免碰撞所需的动作约束。部分可观察的马尔可夫决策过程（POMDP）为不确定性下的规划提供了一个通用模型。但是，操纵任务通常具有较大的动作空间，这不仅使任务计划变得棘手，而且带来了大量的动作计划工作以检查动作的可行性。在这项工作中，提出了一种用于对象操作任务的新型分层POMDP，其中提取了一个简短的抽象POMDP并与原始POMDP一起使用。提出了一种层次化的树型搜索算法，用于有效的在线计划 它通过使用抽象POMDP构建树的一部分来构造更少的置信节点，并通过使用抽象POMDP的观察功能确定动作的可行性来调用运动计划的次数更少。还设计了一种学习机制，以防过渡和观察功能的概率未知。通过对象提取任务演示了此计划框架，并通过模拟和实验对性能进行了经验验证。