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

TL;DR

This paper introduces a ToM-based algorithm utilizing Markov decision processes and hidden Markov models to recognize human intentions in flexible robotized warehouses, enhancing safety and efficiency.

Contribution

It presents a novel ToM-inspired framework combining MDPs and HMMs for human intention recognition in warehouse automation.

Findings

The framework accurately predicts human desires in simulated warehouse scenarios.

It demonstrates effective intention recognition based on observed actions.

The approach is intuitive and adaptable to different goal configurations.

Abstract

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.