The insights from this study can be leveraged to design novel action spaces that enhance the sim-to-real transfer of robot manipulation policies. By focusing on characteristics that have shown to be beneficial for transferability, such as controlling a higher-order derivative of the control variables and minimizing the tracking error, designers can create action spaces that are more robust and effective in real-world applications. For example, incorporating integral terms in delta action spaces, similar to the MI∆JV variant that showed promising results in the study, can help maintain consistency in control targets and reduce the tracking error, leading to smoother and more reliable policies in the real world. Additionally, exploring hybrid action spaces that combine the strengths of different base action spaces, such as integrating joint impedance control with Cartesian velocity control, could potentially offer a versatile approach that balances exploration capabilities and transfer performance.