Safe Reinforcement Learning for Objects Manipulation in Safety-Critical Coordinated Tasks

This paper presents a safety-focused, closed-loop grasping approach for object manipulation in dynamic environments, leveraging simulation data to ensure adaptive and safety-aware operations. We extend the OpenAI Safety Gym library by integrating a robotic arm model and propose’Grasp Mechanics,’ a novel method for adaptive gripping. Using a constrained variant of Proximal Policy Optimization (cPPO), we emphasize secure object manipulation in human-robot interaction scenarios. Preliminary experiments show that cPPO, while requiring longer training, achieves policy performance comparable to baseline PPO and significantly outperforms it in safety compliance. Our work highlights the potential of integrating advanced RL techniques with robust safety mechanisms, advancing the capabilities of robotic systems for real-world applications. These findings lay the groundwork for future advancements in safe autonomous robotics, emphasizing the importance of integrating safety considerations from the ground up.