We acknowledge that precise SyslD is inherently challenging. Based on the results presented in Table 3 (consistent with TABLE I in our paper), our key finding is that the need for SyslD and DR varies significantly depending on the dynamic parameters.
Specifically, for parameters such as mass m and inertia I. which can be measured through conventional methods, accurate SyslD is essential, and domain randomization (DR) should not be applied. Introducing DR for these parameters increases training complexity, often causing the policy to converge to suboptimal solutions.
For the motor time constant Tm, it can be experimentally measured for larger quadrotors. However for smaller quadrotors like the Crazyflie 2.1. direct measurement is impractical. Instead. we reference typical values from DATT, which is an RL-based method, achieving SOTA tracking performance on the Crazyflie 2.1. Experiments in TABLE I demonstrate that sim2real performance is insensitive to Tm, rendering DR unnecessary. DR primarily increases the learning difficulty without providing significant performance benefits.
Regarding the thrust coefficient kf, we estimate it using the force balance equation during stable hovering. At the hover point, the equation 1/4 mg =r*kf*Omega_max^2 holds, where r is the throttle percentage and Omega_max is the maximum rotor speed. Thus, kf can be derived as kf=mg/(4rOmega_max^2). For the Crazyflie 2.1, we obtain Omega_max from the official documentation and measure the throttle percentage during hovering to estimate kf. Results in TABLE I show that sim2real performance is highly sensitive to kf, with parameter deviations leading to significant performance degradation. However, introducing DR substantially improves performance. Based on these observations, we recommend initially obtaining an approximate value of k, through SyslD and avoiding DR in the early stages. If the real-world performance is not so good, DR can be introduced as a potential method for improvement.