Proactive Control for Online Individual User Adaptation in a Welfare Robot Guidance Scenario: Toward Supporting Elderly People

Due to demographic change, health and elderly care systems are facing a shortage of qualified caregivers. This issue can be addressed by introducing welfare robots into people's homes, hospitals, and care institutions. To provide useful support, such robots must adapt to individual users and smoothly interact with them. From this perspective, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: 1) navigation control; 2) visual human detection; and 3) temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments. At the same time, it can predict human walking speed based on visual information without prior knowledge of personality and preferences (i.e., walking speed). The robot then uses this prediction to continuously adapt its speed to individual users in a proactive online manner. We validate the performance of the proposed proactive robot control in different real-world environments with various users, including an elderly resident of a Danish elderly care center. The results show that the robot successfully and smoothly guided various users of different ages and average walking speeds (e.g., 0.2 m/s, 0.7 m/s, and 1.1 m/s) to target locations over distances of 25-60 m. All in all, this study captures a wide range of research from robot control technology development to technological validity in a relevant environment and system prototype demonstration in an operational environment (i.e., an elderly care center).

Automated summary

Due to demographic change, there is a shortage of qualified caregivers in health and elderly care systems. Introducing welfare robots into people's homes, hospitals, and care institutions can address this issue. The proposed control approach integrates navigation control, visual human detection, and neural learning for online individual user adaptation in a welfare robot guidance scenario. The results show that the proactive robot control successfully guided various users in real-world environments.