Semi-mind controlled robots based on Reinforcement Learning for Indoor Application

This paper presents a solution to this challenge by introducing interactive feedback derived from brain signals to train robots using deep reinforcement learning, particularly in the context of indoor maze navigation. Our objective is to enhance the learning process in a human-robot interaction scenario by incorporating human emotion or attention feedback. To accomplish this, we empowered the robot to learn new tasks through a dynamic policy network based on human feedback, and we augmented this input with other sensor data, including LIDAR. Various experiments are conducted to compare the efficacy of manual feedback, brain signal feedback, and no brain signal feedback, employing diverse Reinforcement Learning models. Additionally, we explore different models for emotion classification, employing Graph Neural Network models and traditional deep learning models, and subsequently compare the outcomes.