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The rapid growth of e-commerce increases the need for larger warehouses andtheir automation, thus using robots as assistants to human workers becomes apriority. In order to operate efficiently and safely, robot assistants or thesupervising system should recognize human intentions. Theory of mind (ToM) isan intuitive conception of other agents' mental state, i.e., beliefs anddesires, and how they cause behavior. In this paper we present a ToM-basedalgorithm for human intention recognition in flexible robotized warehouses. Wehave placed the warehouse worker in a simulated 2D environment with threepotential goals. We observe agent's actions and validate them with respect tothe goal locations using a Markov decision process framework. Thoseobservations are then processed by the proposed hidden Markov model frameworkwhich estimated agent's desires. We demonstrate that the proposed frameworkpredicts human warehouse worker's desires in an intuitive manner and in the endwe discuss the simulation results.

Human Intention Recognition in Flexible Robotized Warehouses based on Markov Decision Processes