RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approach

TL;DR

This paper describes how the RoboBreizh team developed a modular system for the Pepper robot, integrating perception and learning techniques, which led to winning the 2020 RoboCup@Home Education challenge.

Contribution

The paper introduces a modular approach combining object detection, pose estimation, and Learning by Demonstration for robot skill acquisition, tailored for the RoboCup@Home Education context.

Findings

Successfully integrated object detection and pose estimation for intention recognition

Used Learning by Demonstration to enhance robot skills

Achieved best performance in the 2020 RoboCup@Home Education challenge

Abstract

Every year, the Robocup@Home competition challenges teams and robots' abilities. In 2020, the RoboCup@Home Education challenge was organized online, altering the usual competition rules. In this paper, we present the latest developments that lead the RoboBreizh team to win the contest. These developments include several modules linked to each other allowing the Pepper robot to understand, act and adapt itself to a local environment. Up-to-date available technologies have been used for navigation and dialogue. First contribution includes combining object detection and pose estimation techniques to detect user's intention. Second contribution involves using Learning by Demonstrations to easily learn new movements that improve the Pepper robot's skills. This proposal won the best performance award of the 2020 RoboCup@Home Education challenge.