Metrics and Benchmarks for Remote Shared Controllers in Industrial Applications
This work addresses the slow adoption of AI in industrial robotics by providing evaluation tools, though it is incremental as it builds on existing shared control research.
The paper tackles the problem of evaluating AI-enhanced remote shared controllers in industrial robotics by proposing benchmarks and metrics, and demonstrates that a simple intelligent controller outperforms manual operation in a tele-operated grasping scenario with a 30% reduction in task completion time.
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.