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 its behavior is reproducible across repeated trials. 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. In comparative evaluations on this subsystem, the data-driven controller achieved lower tracking errors than a traditional PID controller. This result suggests that data-driven approaches are a promising option for controlling complex, high-DOF pneumatic robots.
From: Hiroshi Atsuta [view email]
[v1]
Mon, 16 Mar 2026 03:38:14 UTC (7,318 KB)
[v2]
Fri, 8 May 2026 08:09:42 UTC (7,422 KB)
[v3]
Fri, 11 Sep 2026 03:36:04 UTC (7,423 KB)
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