The tactile-based reinforcement learning approach proposed for robotic insertion tasks has broad applications beyond this specific domain. In medical robotics, this approach could be utilized for tasks such as surgical procedures, where precise and delicate manipulation is crucial. By integrating tactile feedback, robots can enhance their ability to interact with biological tissues and perform intricate surgical tasks with high accuracy and safety. In assistive technologies, tactile-based RL can be employed to develop robotic systems that assist individuals with disabilities in daily activities, such as grasping objects or navigating environments. By adapting the tactile-based RL framework to these domains, robots can improve their interaction capabilities and provide valuable support in various real-world scenarios.