Beyond the action space design, several other factors could contribute to the sim-to-real gap in robot manipulation tasks. One significant factor is the fidelity of the simulation environment compared to the real-world setting. Discrepancies in dynamics, sensor noise, actuator delays, and environmental conditions can all lead to discrepancies between simulation and reality, impacting the transferability of learned policies. Addressing this challenge involves improving the realism of simulations through domain randomization, incorporating noise models, and fine-tuning simulation parameters to better match real-world conditions. Furthermore, the presence of safety mechanisms, such as rate limiters and low-pass filters, in the real-world setup can also affect policy performance and introduce additional complexities in transferring learned behaviors. Ensuring that these safety mechanisms are appropriately integrated and do not hinder policy execution is crucial for successful sim-to-real transfer.