Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications

TL;DR

This paper presents a comprehensive evaluation framework for selecting and identifying learning models to control bionic robots with non-linear dynamics, focusing on transfer function identification for MIMO systems.

Contribution

It introduces a systematic evaluation strategy for model-free control methods, specifically addressing transfer function identification for complex bionic robotic systems.

Findings

Effective data collection and model selection methods are proposed.

Comparative analysis highlights the strengths of different learning models.

A framework for transfer function identification in MIMO robotic data is developed.

Abstract

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.