Human-Machine Interface for Remote Training of Robot Tasks

TL;DR

This paper introduces a novel remote human-machine interface for robot training that combines vision-based hand gesture recognition, a digital twin simulation, and dynamic motion primitives to enable efficient remote robot programming.

Contribution

It presents an integrated system that allows intuitive remote training of robots, combining gesture recognition, simulation, and trajectory learning, which is a new approach in remote robot operation.

Findings

System effectively recognizes hand gestures for robot control

Digital twin provides accurate visual feedback

Remote training with dynamic motion primitives is promising

Abstract

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.