Enhancing human-robot interaction by interpreting uncertain information in navigational commands based on experience and environment

Abstract

Assistive robots can support activities of elderly people to uplift the living standard. The assistive robots should possess the ability to interact with the human peers in a human friendly manner because those systems are intended to be used by non-experts. Humans prefer to use voice instructions that include uncertain information and lexical symbols. Hence, the ability to understand uncertain information is mandatory for developing natural interaction capabilities in robots. This paper proposes a method to understand uncertain information such as “close”, “near” and “far” in navigational user commands based on the current environment and the experience of the robot. A robot experience model (REM) has been introduced to understand the lexical representations in user commands and to adapt the perception of the robot on uncertain information in heterogeneous domestic environments. The user commands are not bounded by a strict grammar model and this enables the users to operate the robot in a more natural way. The proposed method has been implemented on the assistive robot platform. The experiments have been carried out in an artificially created domestic environment and the results have been analyzed to identify the behaviors of the proposed concept.