论文标题
POMDP_PY：建立和解决POMDP问题的框架
pomdp_py: A Framework to Build and Solve POMDP Problems
论文作者
论文摘要
在本文中，我们介绍了POMDP_PY，这是一个通用的马尔可夫决策过程（POMDP）库，写在Python和Cython中。现有的POMDP库通常会由于基础编程语言或接口而导致的可访问性和有效的原型制作，并且需要软件工具链中的额外复杂性才能与机器人系统集成。 POMDP_PY具有简单而全面的接口，能够描述大型离散或连续（PO）MDP问题。在这里，我们总结了设计原理，并详细描述了POMDP_PY中的编程模型和接口。我们还描述了该库与ROS（机器人操作系统）的直观集成，该集成使我们的躯干驱动的机器人能够在3D中执行对象搜索。最后，我们注意到改进和扩展该库以进行POMDP规划及以后的方向。
In this paper, we present pomdp_py, a general purpose Partially Observable Markov Decision Process (POMDP) library written in Python and Cython. Existing POMDP libraries often hinder accessibility and efficient prototyping due to the underlying programming language or interfaces, and require extra complexity in software toolchain to integrate with robotics systems. pomdp_py features simple and comprehensive interfaces capable of describing large discrete or continuous (PO)MDP problems. Here, we summarize the design principles and describe in detail the programming model and interfaces in pomdp_py. We also describe intuitive integration of this library with ROS (Robot Operating System), which enabled our torso-actuated robot to perform object search in 3D. Finally, we note directions to improve and extend this library for POMDP planning and beyond.
