Dual-arm Motion Generation for Repositioning Care based on Deep Predictive Learning with Somatosensory Attention Mechanism

A versatile robot working in a domestic environment based on a deep neural network (DNN) is currently attracting attention. One of the roles expected for domestic robots is caregiving for a human. In particular, we focus on repositioning care because repositioning plays a fundamental role in supporting the health and quality of life of individuals with limited mobility. However, generating motions of the repositioning care, avoiding applying force to non-target parts and applying appropriate force to target parts, remains challenging. In this study, we proposed a DNN-based architecture using visual and somatosensory attention mechanisms that can generate dual-arm repositioning motions which involve different sequential policies of interaction force; contact-less reaching and contact-based assisting motions. We used the humanoid robot Dry-AIREC, which features the capability to adjust joint impedance dynamically. In the experiment, the repositioning assistance from the supine position to the sitting position was conducted by Dry-AIREC. The trained model, utilizing the proposed architecture, successfully guided the robot's hand to the back of the mannequin without excessive contact force on the mannequin and provided adequate support and appropriate contact for postural adjustment.