A Data Collection Scheme to Develop Future Autonomous Manipulation for Military Applications

Abstract

The U.S. Department of Defense is advancing mobile robotic manipulation to conduct dangerous tasks such as Explosive Ordnance Disposal (EOD) and hazardous materials (HazMat) handling, where current autonomous systems fall short. In a battlefield environment, contested communications may prohibit the use of telemanipulation, thus establishing the need for highly dexterous autonomous mobile manipulation robots. This paper presents a data collection scheme using telemanipulation, comprised of a Mixed Reality (MR) control interface, enabling a human in the loop to remotely execute a specialized task. The user-interaction data collected is instrumental in developing advanced, predictive, and adaptive control systems via machine learning algorithms. These systems enhance robot autonomy while ensuring operator oversight, particularly critical in military settings. We detail an initial pick-and-place task with novice cadet researchers, analyzing the results and setting the stage for future research in autonomous mobile manipulation for battlefield applications.