Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications
This work addresses a practical challenge for developers of bionic robots who need to implement model-free control strategies, but it appears incremental as it focuses on evaluation rather than new methods.
This research tackles the problem of selecting appropriate learning models for bionic robots with non-linear elastic dynamics by introducing a comprehensive evaluation framework for model-free control, including data collection, model selection, comparative analysis, and transfer function identification to handle MIMO robotic data.
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.