Combining safety-guided data collection with other imitation learning techniques, such as inverse reinforcement learning (IRL), can offer several benefits in enhancing the performance and robustness of learned policies. By incorporating IRL, the agent can learn the underlying reward structure of the expert's behavior, allowing for a more nuanced understanding of the task objectives. This can complement the safety-guided data collection by providing additional insights into expert behavior in different contexts. Furthermore, the combination of safety guidance with IRL can help in capturing complex decision-making processes and preferences of the expert, leading to more adaptive and versatile learned policies. Overall, integrating safety-guided data collection with IRL can result in more comprehensive and effective imitation learning frameworks for challenging robotic tasks.