Human-in-the-loop transfer learning in collision avoidance of autonomous robots

Abstract

Neural networks have demonstrated exceptional performance across a range of applications. Yet, their training often demands substantial time and data resources, presenting a challenge for autonomous robots operating in real-world environments where real-time learning is difficult. To mitigate this constraint, we propose a novel human-in-the-loop framework that harnesses human expertise to mitigate the learning challenges of autonomous robots. Our approach centers on directly incorporating human knowledge and insights into the robot’s learning pipeline. The proposed framework incorporates a mechanism for autonomous learning from the environment via reinforcement learning, utilizing a pre-trained model that encapsulates human knowledge as its foundation. By integrating human-provided knowledge and evaluation, we aim to bridge the division between human intuition and machine learning capabilities. Through a series of collision avoidance experiments, we validated that incorporating human knowledge significantly improves both learning efficiency and generalization capabilities. This collaborative learning paradigm enables robots to utilize human common sense and domain-specific expertise, resulting in faster convergence and better performance in complex environments. This research contributes to the development of more efficient and adaptable autonomous robots and seeks to analyze how humans can effectively participate in robot learning and the effects of such participation, illuminating the intricate interplay between human cognition and artificial intelligence.