论文标题
机器人技术中部分可观察到的马尔可夫决策过程：一项调查
Partially Observable Markov Decision Processes in Robotics: A Survey
论文作者
论文摘要
嘈杂的传感，不完美的控制和环境变化是许多现实世界机器人任务的特征。部分可观察到的马尔可夫决策过程（POMDP）提供了一个原则上的数学框架，用于建模和解决不确定性下的机器人决策和控制任务。在过去的十年中，它看到了许多成功的应用程序，涵盖了本地化和导航，搜索和跟踪，自动驾驶，多机器人系统，操纵和人类机器人的互动。这项调查旨在弥合POMDP模型的开发与算法之间的差距，以及针对另一端的机器人决策任务的应用。它分析了这些任务的特征，并将它们与POMDP框架的数学和算法属性联系起来，用于有效建模和解决方案。对于从业者来说，调查提供了一些关键的任务特征，以决定成功地将POMDP应用于机器人任务。对于POMDP算法设计师，该调查为将POMDP应用于机器人系统的独特挑战提供了新的见解，并指出了有希望的新方向进行进一步研究。
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research.
