Remote manipulation is emerging as one of the key robotics tasks needed in extreme environments. Several researchers have investigated how to add AI components into shared controllers to improve their reliability. Nonetheless, the impact of novel research approaches in real-world applications can have a very slow in-take. We propose a set of benchmarks and metrics to evaluate how the AI components of remote shared control algorithms can improve the effectiveness of such frameworks for real industrial applications. We also present an empirical evaluation of a simple intelligent share controller against a manually operated manipulator in a tele-operated grasping scenario.
@article{arxiv.1906.08381,
title = {Metrics and Benchmarks for Remote Shared Controllers in Industrial Applications},
author = {Claudio Zito and Maxime Adjigble and Brice D. Denoun and Lorenzo Jamone and Miles Hansard and Rustam Stolkin},
journal= {arXiv preprint arXiv:1906.08381},
year = {2019}
}