Personalization of Robot Behavior Using Approach Based on Model Predictive Control

摘要

This paper proposes a novel approach to personalizing robot behavior using Model Predictive Control (MPC). Social humanoid robots, equipped with advanced sensors and human-like capabilities, are increasingly integrated into human environments, necessitating adaptable and intuitive communication interfaces. Our approach enables the design of adaptive interfaces that support fluid, personalized human–robot interactions. In the proposed framework, a user model is applied to predict responses to potential robot actions. Initially, this model represents an average user; however, it is updated as the robot gathers new observations, leading to increasingly personalized decisions. Experiments assessed the performance of five machine-learning algorithms generating user models in a simulated environment, with the Light Gradient Boosting Machine (LGBM) achieving the best results, closely followed by Random Forest (RF). A comparison of the inference time showed that LGBM is more than four times faster than RF. Outlier-removal techniques showed a modest performance improvement over models without outlier removal. Additionally, robot adaptation was tested in the experiments, showing an increase in the average reward over time, although with a relatively high standard deviation. The results suggest that the proposed approach for robot behavior adaptation based on MPC works well, and the recommended algorithm for the user model is LGBM.