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.

翻译：具有高自由度的气动驱动拟人机器人在物理人机交互领域展现出巨大潜力。然而，由于气动执行器固有的非线性特性，其精确控制具有挑战性。本文介绍了一种紧凑型13自由度上半身人形机器人的研制。为评估有效控制器的可行性，我们首先研究了其关键动态特性（如驱动时间延迟），并确认该系统表现出高度可复现的行为。利用这一可复现性，我们基于具有显式时间延迟补偿的多层感知器，为4自由度手臂子系统实现了初步的数据驱动控制器。该网络通过随机运动数据进行训练，以生成用于跟踪任意轨迹的压力指令。与传统PID控制器的对比评估表明，该方法在轨迹跟踪性能上具有显著优势，凸显了数据驱动方法在控制复杂高自由度气动机器人方面的潜力。