# Inertial Parameter Identification in Robotics: A Survey
## 10 Results, Discussion and Perspectives
### General Discussion and Perspectives  
The present discussion is somehow related to the Epilogue of [66] where the author gives an insightful presentation on what can be considered to be '_good_', '_bad_' or '_optimal_'. Even though the underlying goal of benchmarking is to _find_ the best identification method, a _black and white_ answer can seldom be obtained. Indeed, if the choice of a method often depends on the circumstances of identification, namely _how_ the dynamic parameters of a robot can be identified, it may sometimes depend on the final objectives of identification, or in other words _why_ these parameters are being identified. Based on the different Monte Carlo simulations performed in this paper, the IDIM-IV and DIDIM methods seem to be the most appealing for offline identification, as they converge quickly, do not require custom data-filtering, combine the inverse and direct dynamic models and are well-suited to the enforcement of physical constraints through semi-definite programming. Since the dynamic models are validated simultaneously, a complete robot simulator can be obtained, making it possible to design an accurate model-based control. Besides the fact that these algorithms are difficult to implement in real-time control loops--thereby raising serious issues regarding their implementation in adaptive control strategies--their main limitations directly stems from the need for a precise knowledge of the robot's low-level control characteristics. This dependence is clearly observable when comparing the MCS results--where the control law is assumed to be perfectly known--and that obtained with the real systems. In practice, the incomplete knowledge regarding the true nature of the implemented low-level control loops has tangible consequences on the behavior of the internal dynamic simulation routine of IDIM-IV and DIDIM, and more generally of CLIE, CLOE and NKF. This in turn induces a bias in the parameter estimates and may even in the worst case lead to a divergence in the identification process, as the behavior of the simulation routine no longer matches that of the real system. Please note that this statement can be refined by distinguishing between the ideal case where a good prior knowledge of the low-level control structure and gains is available, the case where only the control structure is known, and the case where neither the control structure nor the gains are known. In practice, knowing the structure of a controller makes it possible to identify its gains, as exposed in [52]. The fact that IDIM-OLS, -WLS, -TLS, ML, AdaNN and HTRNN do not require any prior knowledge of the low-level control characteristics, and that their performance does not depend on any initial estimate or tuning coefficients, makes them fairly easy to implement and therefore attractive to the practitioner. It is also worth mentioning that NKF, IDIM-OLS and WLS are, or can be formulated recursively and are therefore suitable for implementation in real-time adaptive control loops. As already pointed out in [7], the main drawback of Kalman filtering techniques in the context of parameter identification seems to be their extreme sensitivity to the initial values of a set of adjustment parameters, and in particular to the values of the initial process-noise and measurement-noise covariance. In practice, the tuning process proves to be tedious although the computed torque approach would deserve to be further investigated. Therefore, in case the objective of identification is not the offline refinement of CAD parameters values for reliable simulation purpose, but rather consists of developing robust controllers based on an online rough estimation of the robot inertial parameters, the IDIM-OLS, -WLS and NKF methods, appear to be suitable choices. It should be noted that a more detailed study of the effects of different amplitudes or statistical distributions of noise, resulting for example from higher quantization errors--i.e., lower sensor resolutions--would be relevant and is here considered to be future work.