Learning to Master Robotic Arm Movements with Bimanual Joystick Control: Indicators for Evaluating the Difficulty of Movement Tasks

Abstract

Manual control of robotic arms is challenging and productive operators require extensive prior training. Effective training should systematically vary the difficulty level of the robot arm motions. This study investigates the extent to which Fitts’ law could define movement difficulty for bimanual controlled movements of robotic arms. Inspired by forestry work-methods, we designed Fitts’ tapping Task to assess the movement time and throughput of ten unskilled participants over nine training sessions. We found that robotic arm movements observe Fitts’ law for reaching in depth but deviate for lateral and concentric movements. In other words, training can utilize Fitts’ law to vary the difficulty of forward robot arm movements. Further studies on the difficulty of lateral and concentric movements are necessary to refine work methods and improve training.