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.

翻译：仿生机器人动力学的控制与建模越来越多地采用基于机器学习的无模型控制策略。鉴于仿生机器人系统的非线性弹性特性，基于学习的方法利用数值数据建立从驱动输入到机器人轨迹的直接映射，而无需复杂的运动学模型，从而提供了可靠的替代方案。然而，对于开发者而言，如何为其特定仿生机器人选择合适的机器学习模型并进一步构建传递函数的方法尚未得到充分探讨。因此，本研究训练了四种适用于多输入多输出（MIMO）数据和非线性传递函数辨识的模型，包括集成学习模型、基于正则化的模型、基于核的模型以及神经网络模型，以评估它们的（1）准确性、（2）计算复杂度以及（3）捕捉生物运动的表现。本研究涵盖了控制输入与动作输出的数据采集方法、机器学习模型的选择、训练结果的比较分析以及传递函数辨识。主要目标是为无模型控制的应用提供一个全面的评估策略与框架。