From Canada to Japan: How 10,000 km Affect User Perception in Robot Teleoperation

Robot teleoperation (RTo) has emerged as a viable alternative to local control, particularly when human intervention is still necessary. This research aims to study the distance effect on user perception in RTo, exploring the potential of teleoperated robots for older adult care. We propose an evaluation of non-expert users' perception of long-distance RTo, examining how their perception changes before and after interaction, as well as comparing it to that of locally operated robots. We have designed a specific protocol consisting of multiple questionnaires, along with a dedicated software architecture using the Robotics Operating System (ROS) and Unity. The results revealed no statistically significant differences between the local and remote robot conditions, suggesting that robots may be a viable alternative to traditional local control.

Leveraging GCN-based Action Recognition for Teleoperation in Daily Activity Assistance

Caregiving of older adults is an urgent global challenge, with many older adults preferring to age in place rather than enter residential care. However, providing adequate home-based assistance remains difficult, particularly in geographically vast regions. Teleoperated robots offer a promising solution, but conventional motion-mapping teleoperation imposes unnatural movement constraints on operators, leading to muscle fatigue and reduced usability. This paper presents a novel teleoperation framework that leverages action recognition to enable intuitive remote robot control. Using our simplified Spatio-Temporal Graph Convolutional Network (S-ST-GCN), the system recognizes human actions and executes corresponding preset robot trajectories, eliminating the need for direct motion synchronization. A finite-state machine (FSM) is integrated to enhance reliability by filtering out misclassified actions. Our experiments demonstrate that the proposed framework enables effortless operator movement while ensuring accurate robot execution. This proof-of-concept study highlights the potential of teleoperation with action recognition for enabling caregivers to remotely assist older adults during activities of daily living (ADLs). Future work will focus on improving the S-ST-GCN's recognition accuracy and generalization, integrating advanced motion planning techniques to further enhance robotic autonomy in older adult care, and conducting a user study to evaluate the system's telepresence and ease of control.