Dynamic Properties and Motion Reproducibility of a Compact Pneumatically Actuated Humanoid Upper Body for Data-Driven Control

TL;DR

This paper introduces a compact 13-DOF pneumatic humanoid upper body, analyzes its dynamic properties, and demonstrates that a data-driven neural network controller outperforms traditional PID control in trajectory tracking.

Contribution

It develops a reproducible pneumatic humanoid robot and applies a neural network-based data-driven controller for improved motion control.

Findings

Reproducible dynamic behavior of the robot confirmed.

Neural network controller outperforms PID in trajectory tracking.

Effective pressure command generation for arbitrary movements.

Abstract

Pneumatically-actuated anthropomorphic robots with high degrees of freedom (DOF) offer significant potential for physical human-robot interaction. However, precise control of pneumatic actuators is challenging due to their inherent nonlinearities. This paper presents the development of a compact 13-DOF upper-body humanoid robot. To assess the feasibility of an effective controller, we first investigate its key dynamic properties, such as actuation time delays, and confirm that the system exhibits highly reproducible behavior. Leveraging this reproducibility, we implement a preliminary data-driven controller for a 4-DOF arm subsystem based on a multilayer perceptron with explicit time delay compensation. The network was trained on random movement data to generate pressure commands for tracking arbitrary trajectories. Comparative evaluations with a traditional PID controller demonstrate superior trajectory tracking performance, highlighting the potential of data-driven approaches for controlling complex, high-DOF pneumatic robots.