基于马尔可夫决策过程的柔性机器人仓库人意图识别
Human Intention Recognition in Flexible Robotized Warehouses based on Markov Decision Processes
摘要：
电子商务的快速增长增加了对大型仓库及其自动化的需求，因此使用机器人作为人类工人的助手成为优先事项。为了有效和安全地操作，机器人助手或监督系统应该识别人类的意图。心理理论（Theory of Mind，ToM）是对其他主体心理状态的直观概念，即，信仰和欲望，以及它们如何导致行为。在本文中，我们提出了一个基于ToM的算法，在灵活的机器人仓库的人类意图识别。我们将仓库工人置于模拟的2D环境中，其中有三个潜在目标。我们观察代理的行动，并验证他们的目标位置使用马尔可夫决策过程框架。然后，这些观察结果由所提出的隐马尔可夫模型框架进行处理，该框架估计代理的愿望。我们证明了所提出的框架预测人类仓库工人的愿望在一个直观的方式，并在最后，我们讨论了模拟结果。
英文摘要：
The rapid growth of e-commerce increases the need for larger warehouses and their automation, thus using robots as assistants to human workers becomes a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions. Theory of mind (ToM) is an intuitive conception of other agents' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we present a ToM-based algorithm for human intention recognition in flexible robotized warehouses. We have placed the warehouse worker in a simulated 2D environment with three potential goals. We observe agent's actions and validate them with respect to the goal locations using a Markov decision process framework. Those observations are then processed by the proposed hidden Markov model framework which estimated agent's desires. We demonstrate that the proposed framework predicts human warehouse worker's desires in an intuitive manner and in the end we discuss the simulation results.
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