RNN-Based Shared Control for Enhanced Sense of Agency in Robotic Teleoperation

Robotic teleoperation, where humans operate robots at remote locations, removes temporal and spatial barriers and enables physical interaction with people and objects. Proactive robot operation is expected to improve the operator's motivation and sense of self-efficacy. However, when manipulation interventions that support task execution occur, the SoA (Sense of Agency: the sense of perception that the movement of the observed object is caused by oneself) decreases. Therefore, a method of manipulation intervention that maintains a high SoA is required. We proposed a shared control method in which the robot motion does not deviate significantly from the operator's intention by predicting the next time step input in real time with an RNN model that learns from the skilled operator's operations. We experimented with pouring tasks under different conditions, using a system where two remote robot arms were controlled by a VR device, the operator's hand and head position and posture were synchronized with the robot, and the robot's viewpoint was shared. Experimental results confirmed that in some cases, the proposed method corrects the operation input temporally and spatially so that it approaches the trajectory of the skilled operator. Although no significant difference was obtained in the improvement of task performance by the proposed method, it was confirmed that the proposed method maintained a higher SoA than the motion playback of the skilled operator's operation.