History-Aware Force Sensing and Tunable Stiffness in a Pneumatic Kresling Origami Cell
History-Aware Force Sensing and Tunable Stiffness in a Pneumatic Kresling Origami Cell
This research presents a 3D-printed PEBA Kresling origami cell equipped with magnetic angular sensing that supports three operating modes: force sensing at zero gauge pressure, tunable stiffness under positive pressure, and self-sensing actuation under negative pressure.
The study demonstrates a compact soft origami cell capable of force sensing and tunable stiffness across positive, zero, and negative gauge pressures using a multiscale GRU network, with reported RMSEs ranging from 0.76 N to 2.10 N depending on operating conditions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
RMSE at zero gauge pressure
0.76
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, and self-sensing actuation under negative gauge pressure. A multiscale gated recurrent unit (GRU) network estimates contact force from angular histories to account for the the combined effects of geometric deformation and material response. At zero gauge pressure, the model achieves a test RMSE of 0.76~N. Positive gauge pressure approximately doubles the average axial loading stiffness at +20~kPa over the first 3~m
Open source S3RMSE under positive gauge pressure
<0.93
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m of compression, while pressure-specific models maintain RMSEs below 0.93~N. Under negative gauge pressure, an energy-based compensation model enables contact force estimation using the the model trained at zero gauge pressure, achieving RMSEs of 1.66--2.10~N under quasi-static conditions. These results demonstrate a compact approach to combining force feedback, tunable axial stiffness, and self-sensing actuation in
Open source S4RMSE under negative gauge pressure
1.66
View original evidence
m of compression, while pressure-specific models maintain RMSEs below 0.93~N. Under negative gauge pressure, an energy-based compensation model enables contact force estimation using the the model trained at zero gauge pressure, achieving RMSEs of 1.66--2.10~N under quasi-static conditions. These results demonstrate a compact approach to combining force feedback, tunable axial stiffness, and self-sensing actuation in
Open source S4RMSE under negative gauge pressure
2.1
View original evidence
m of compression, while pressure-specific models maintain RMSEs below 0.93~N. Under negative gauge pressure, an energy-based compensation model enables contact force estimation using the the model trained at zero gauge pressure, achieving RMSEs of 1.66--2.10~N under quasi-static conditions. These results demonstrate a compact approach to combining force feedback, tunable axial stiffness, and self-sensing actuation in
Open source S4compression distance
3
View original evidence
, and self-sensing actuation under negative gauge pressure. A multiscale gated recurrent unit (GRU) network estimates contact force from angular histories to account for the the combined effects of geometric deformation and material response. At zero gauge pressure, the model achieves a test RMSE of 0.76~N. Positive gauge pressure approximately doubles the average axial loading stiffness at +20~kPa over the first 3~m
Open source S3
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
, and self-sensing actuation under negative gauge pressure. A multiscale gated recurrent unit (GRU) network estimates contact force from angular histories to account for the the combined effects of geometric deformation and material response. At zero gauge pressure, the model achieves a test RMSE of 0.76~N. Positive gauge pressure approximately doubles the average axial loading stiffness at +20~kPa over the first 3~m
Open source S3
m of compression, while pressure-specific models maintain RMSEs below 0.93~N. Under negative gauge pressure, an energy-based compensation model enables contact force estimation using the the model trained at zero gauge pressure, achieving RMSEs of 1.66--2.10~N under quasi-static conditions. These results demonstrate a compact approach to combining force feedback, tunable axial stiffness, and self-sensing actuation in
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org