仿生机器人学习模型的比较评估：非线性传递函数辨识
Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications
AI总结：
针对仿生机器人无模型控制中合适学习模型选择及传递函数构建缺乏系统探讨的问题，本研究提出涵盖数据采集、模型选择等环节的综合评估框架，可有效处理MIMO机器人数据。
摘要：
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，无需复杂的运动学模型，提供了可靠的替代方案。然而，对于开发者而言，如何为其特定的仿生机器人选择合适的学习模型并进一步构建传递函数，尚未得到充分探讨。因此，本研究提出了一套适用于无模型控制的综合评估策略与框架，涵盖数据采集、学习模型选择、对比分析及传递函数辨识，可有效处理多输入多输出（MIMO）机器人数据。
英文摘要：
The control and modeling of robot dynamics have increasingly adopted model-free control strategies using machine learning. 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 introduces a comprehensive evaluation strategy and framework for the application of model-free control, including data collection, learning model selection, comparative analysis, and transfer function identification to effectively deal with the multi-input multi-output (MIMO) robotic data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
