IJMERR, Volume 14, Number 3, 2025

Cover Story (view full-size image): Ensuring a high level of safety is essential for collision avoidance in real-world robotic applications. Traditional Reinforcement Learning (RL)-based collision avoidance methods offer adaptability but lack safety guarantees, especially in uncertain and dynamic environments. To address this, we propose a novel safe reinforcement learning (SafeRL) framework called Control Recovery and Barrier Function (CRBF), which enhances safety by sequentially applying different control strategies based on the robot’s proximity to obstacles. The CRBF categorizes risk into three distinct levels and adaptively switches between a vanilla RL-based policy, Control Barrier Function (CBF), and a Recovery Function (RF) to prevent collisions and recover from critical situations. In addition, we introduce a constraint-aware training strategy that incorporates these sequential safety mechanisms during policy updates. We validate our method in both simulated and real-world environments, where CRBF outperforms conventional method...View this paper