aiqbench analysis
This research demonstrates the viability of multilayer perceptron-based controllers for managing nonlinearities in high-DOF pneumatic humanoid robots, achieving superior trajectory tracking compared to traditional PID methods. By confirming motion reproducibility and compensating for actuation delays, the study establishes a foundational step toward data-driven control strategies essential for complex physical human-robot interaction.
Dynamic Properties and Motion Reproducibility of a Compact Pneumatically Actuated Humanoid Upper Body for Data-Driven Control
arXiv:2603.14787v3 Announce Type: replace 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.
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.
26 pages, 21 figures, 3 tables. Accepted for publiaction in Advanced Robotics
Robotics (cs.RO)
arXiv:2603.14787 [cs.RO]
(or arXiv:2603.14787v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.14787 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 248 words
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