Integrating convolutional neural networks and reinforcement learning for autonomous person-following in social robotics (2024)
The integration of advanced robotics with sophisticated machine learning techniques offers substantial opportunities to enhance the autonomous capabilities of social robots. This paper presents a novel approach that enables a Pepper-type social robot to autonomously follow a person. Our methodology combines Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL) within a modular architecture. This architecture utilizes both 2D and 3D cameras as sensors, which provide robust real-time detection and tracking, while RL allows adaptive movement control. We have tested our implementation, demonstrating high performance and reliability across various complex environments. The results from these tests confirm that the proposed architecture and learning methods greatly enhance the robot’s ability to autonomously follow a person. This research represents a significant advancement in the practical deployment of autonomous systems in the field of social robotics.