Trust-Calibrated Human-in-the-Loop Reinforcement Learning for Safe and Efficient Autonomous Navigation

Autonomous navigation technology for autonomous ground vehicles (AGVs) is currently a highly active research area. With the advancement of internet of things (IoT) technologies, AGVs increasingly leverage interconnected systems, such as onboard sensors, vehicle-to-everything (V2X) communication, and cloud data sharing, to enhance navigation capabilities. Human-in-the-loop (HIL) guidance has been shown to be effective in improving the performance of reinforcement learning (RL) algorithms. Existing methods in this field often assume that human guidance is always beneficial. However, incorrect guidance can cause oscillations or even divergences in RL training. In this study, we propose an innovative trust-calibrated HIL-RL approach to address these gaps. First, human guidance is introduced into the RL framework to enhance learning performance through intervention and demonstration. This process includes adding behavior cloning (BC) objectives to the RL policy and an adaptive experience replay mechanism. Second, a trust evaluation mechanism is incorporated within the HIL-RL framework to calculate a belief value, which not only ensures that human guidance is trustworthy but also dynamically optimizes the BC weight. This improvement enhances the training efficiency of RL agents and supports steady performance improvement, even when exposed to potentially detrimental external intervention. The results in simulation show that the proposed method achieves an improvement in success rate of 22–26% over vanilla RL. In real-world experiments, the proposed method achieved a 100% success rate and demonstrated outstanding navigation efficiency, validating the effectiveness of the trust evaluation mechanism.