Human-Machine Interface for Remote Training of Robot Tasks
This addresses the need for remote training in applications like online courses or robot farms, but it appears incremental as it combines existing components into a new interface.
The paper tackles the problem of remote robot training by proposing a human-machine interface with vision-based hand detection, gesture recognition, and a digital twin, resulting in a promising solution that eliminates delays and inefficiency.
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.