  The control and modeling of bionic robot dynamics have increasingly adopted
model-free control strategies using machine learning methods. Given the
non-linear elastic nature of bionic robotic systems, learning-based methods
provide reliable alternatives by utilizing numerical data to establish a direct
mapping from actuation inputs to robot trajectories without complex kinematics
models. However, for developers, the method of identifying an appropriate
learning model for their specific bionic robots and further constructing the
transfer function has not been thoroughly discussed. Thus, this research trains
four types of models, including ensemble learning models, regularization-based
models, kernel-based models, and neural network models, suitable for
multi-input multi-output (MIMO) data and non-linear transfer function
identification, in order to evaluate their (1) accuracy, (2) computation
complexity, and (3) performance of capturing biological movements. This
research encompasses data collection methods for control inputs and action
outputs, selection of machine learning models, comparative analysis of training
results, and transfer function identifications. The main objective is to
provide a comprehensive evaluation strategy and framework for the application
of model-free control.
