Multimodal Object Categorization with Reduced User Load through Human-Robot Interaction in Mixed Reality

Abstract

Enabling robots to learn from interactions with users is essential to perform service tasks. However, as a robot categorizes objects from multimodal information obtained by its sensors during interactive onsite teaching, the inferred names of unknown objects do not always match the human user's expectation, especially when the robot is introduced to new environments. Confirming the learning results through natural speech interaction with the robot often puts an additional burden on the user who can only listen to the robot to validate the results. Therefore, we propose a human-robot interface to reduce the burden on the user by visualizing the inferred results in mixed reality (MR). In particular, we evaluate the proposed interface on the system usability scale (SUS) and the NASA task load index (NASA-TLX) with three experimental object categorization scenarios based on multimodal latent Dirichlet allocation (MLDA) in which the robot: 1) does not share the inferred results with the user at all, 2) shares the inferred results through speech interaction with the user (baseline), and 3) shares the inferred results with the user through an MR interface (proposed). We show that providing feedback through an MR interface significantly reduces the temporal, physical, and mental burden on the human user compared to speech interaction with the robot.