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

TL;DR

This paper introduces a deep learning-based approach with somatosensory attention for dual-arm robots to perform repositioning care tasks, improving interaction flexibility and safety.

Contribution

It presents a novel neural network architecture combining proprioceptive and visual attention mechanisms with impedance control for repositioning tasks.

Findings

Successfully generated motions for repositioning without excessive force

Enabled transition from supine to lifted-up position

Demonstrated effectiveness on dual-arm humanoid robot Dry-AIREC

Abstract

Caregiving is a vital role for domestic robots, especially the repositioning care has immense societal value, critically improving the health and quality of life of individuals with limited mobility. However, repositioning task is a challenging area of research, as it requires robots to adapt their motions while interacting flexibly with patients. The task involves several key challenges: (1) applying appropriate force to specific target areas; (2) performing multiple actions seamlessly, each requiring different force application policies; and (3) motion adaptation under uncertain positional conditions. To address these, we propose a deep neural network (DNN)-based architecture utilizing proprioceptive and visual attention mechanisms, along with impedance control to regulate the robot's movements. Using the dual-arm humanoid robot Dry-AIREC, the proposed model successfully generated motions to insert the robot's hand between the bed and a mannequin's back without applying excessive force, and it supported the transition from a supine to a lifted-up position. The project page is here: https://sites.google.com/view/caregiving-robot-airec/repositioning