Bidirectional Operation Prediction for Body Integration System

The body integration system in which multiple users co-operate a single-robot avatar improves operability. However, collaboration among users is essential for smooth operation. In this study, we proposed bidirectional operation predictions for the actions of each operator with their partner in the body integration system. The robot arm and gripper are controlled by two operators. At the same time, based on each operator’s actions, the system predicts the operations 0.3 seconds ahead using machine learning, and the prediction results are visually presented to the other operator. To confirm the operability of the system and its effect on cooperation between operators, we conducted pick-and-place experiments under conditions with and without bidirectional prediction. The results of the subjective evaluation and operation performance suggested that the subjective rating in smooth cooperation was significantly improved, and using the prediction could improve the similarity in the operations of two users who provided a small mental workload and shortened psychological distance.