Abstract

Research on Human-Robot Interaction (HRI) requires the substantial consideration of an experimental design, as well as a significant amount of time to practice the subject experiment. Recent technology in virtual reality (VR) can potentially address these time and effort challenges. The significant advantages of VR systems for HRI are: 1) cost reduction, as experimental facilities are not required in a real environment; 2) provision of the same environmental and embodied interaction conditions to test subjects; 3) visualization of arbitrary information and situations that cannot occur in reality, such as playback of past experiences, and 4) ease of access to an immersive and natural interface for robot/avatar teleoperations. Although VR tools with their features have been applied and developed in previous HRI research, all-encompassing tools or frameworks remain unavailable. In particular, the benefits of integration with cloud computing have not been comprehensively considered. Hence, the purpose of this study is to propose a research platform that can comprehensively provide the elements required for HRI research by integrating VR and cloud technologies. To realize a flexible and reusable system, we developed a real-time bridging mechanism between the robot operating system (ROS) and Unity. To confirm the feasibility of the system in a practical HRI scenario, we applied the proposed system to three case studies, including a robot competition named RoboCup@Home. via these case studies, we validated the system’s usefulness and its potential for the development and evaluation of social intelligence via multimodal HRI.

Highlights

  • Human-robot interaction (HRI) is one of the most active research interest in robotics and intelligent systems

  • We focus on the realization of an open platform to collect and leverage multimodal-interaction-experience data that were collected in daily life environments and require embodied social interaction

  • We developed an open software platform to accelerate HRI research based on the integration of the robot operating system (ROS) and Unity framework with cloud computing

Read more

Summary

Introduction

Human-robot interaction (HRI) is one of the most active research interest in robotics and intelligent systems. Owing to the complexity of the HRI system, there are several challenges facing its research activities One of such challenges is the collection of a dataset for machine learning in HRI (Amershi et al, 2014), which is required to learn and model human activities. Kanda (Kanda et al, 2010) developed a massive sensor network system to observe human activity in a shopping mall environment, over a period of 25 days. Another application of the interaction between a robot and children in an elementary school required approximately two months to collect the interaction dataset (Kanda et al, 2007). The significant cost of such an observation is a limitation of HRI research

Objectives
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call