Enabling Socially Competent navigation through incorporating HRI

TL;DR

This paper introduces a prototype framework that improves social competence in robot navigation by recognizing humans as interactive agents, enabling more natural interactions in indoor environments.

Contribution

It presents the first framework integrating human labeling as interactive agents into robot navigation, using open-source ROS and deep learning tools.

Findings

Enhanced social navigation capabilities demonstrated

Integration of human detection with navigation planning

Open-source implementation available for further research

Abstract

Over the last years, social robots have been deployed in public environments making evident the need of human-aware navigation capabilities. In this regard, the robotics community have made efforts to include proxemics or social conventions within the navigation approaches. Nevertheless, few works have tackled the problem of labelling humans as an interactive agent when blocking the robot motion trajectory. Current state of the art navigation planners will either propose an alternative path or freeze the motion until the path is free. We present the first prototype of a framework designed to enhance social competency of robots while navigating in indoor environments. The implementation is done using Navigation and Object Detection open-source software. Specifically, the Robot Operating System (ROS) navigation stack, and OpenCV with Caffe deep learning models and MobileNet Single Shot Detector (SSD), respectively.