The research trend of the new generation of robots is to make robots participate in human life, and improve the naturalness and flexibility of interaction between humans and robots through human-robot interaction technology. Compared with the traditional Q-learning method, the training times required by the proposed model are reduced by ( N 2 − N)/4, where N is the number of intentions. With the spike-timing-dependent plasticity (STDP) mechanisms and the simple feedback of right or wrong, the humanoid robot NAO could successfully predict the user's intentions in Human Intention Prediction Experiment and Trajectory Tracking Experiment. Based on the neural mechanism of reinforcement learning, we propose a brain-inspired intention prediction model to enable the robot to perform actions according to the user's intention. To make that robots can better serve human beings, adaptable, simple, and flexible human-robot interaction technology is essential.
Although these methods are simple and effective, they lack some flexibility, especially when the programming program is contrary to user habits, which will lead to a significant decline in user experience satisfaction.
Most of the human-robot interaction technologies currently applied to home service robots are programmed by the manufacturer first, and then instruct the user to trigger the implementation through voice commands or gesture commands. With the development of artificial intelligence and robotic technology in recent years, robots are gradually integrated into human daily life.
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