Iterative Adversarial Learning With Chaser Agents for Time-Efficient Crowd-Aware Navigation

Abstract

This paper addresses the challenge of safe and time-efficient crowd navigation for autonomous robots in dynamic environments. Existing methods struggle in scenarios with unpredictable or obstructive pedestrian behaviors. These limitations raise serious safety and efficiency concerns in real-world deployments. To improve navigation robustness and efficiency, we propose an adversarial deep reinforcement learning (DRL) framework that simulates competitive pedestrian behaviors through a chaser agent. In addition, we further introduce an ensemble agent that dynamically selects policies based on real-time observations, enhancing generalization across diverse scenarios. Extensive simulation results demonstrate enhanced navigation performance in terms of success rate and navigation efficiency when using our framework. Additionally, the ensemble agent further improves stability and overall generalizability across various environments. Real-world experiments validate the approach and demonstrate the potential for sim-to-real transfer.