# Metrics and Benchmarks for Remote Shared Controllers in Industrial Applications

arXiv: https://arxiv.org/abs/1906.08381 · HTML: https://mlsanity.com/paper/1906.08381/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Underwater Object Classification and Detection: first results and open challenges](https://mlsanity.com/paper/2201.00977/) (2022)
- [Robot Grasping and Manipulation: A Prospective](https://mlsanity.com/paper/2303.07807/) (2023)

## Abstract (arXiv)

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.