Paper
Optically Sensorized Electro-Ribbon Actuator (OS-ERA)
Authors
Carolina Gay, Petr Trunin, Diana Cafiso, Yuejun Xu, Majid Taghavi, Lucia Beccai
Abstract
Electro-Ribbon Actuators (ERAs) are lightweight flexural actuators that exhibit ultrahigh displacement and fast movement. However, their embedded sensing relies on capacitive sensors with limited precision, which hinders accurate control. We introduce OS-ERA, an optically sensorized ERA that yields reliable proprioceptive information, and we focus on the design and integration of a sensing solution without affecting actuation. To analyse the complex curvature of an ERA in motion, we design and embed two soft optical waveguide sensors. A classifier is trained to map the sensing signals in order to distinguish eight bending states. We validate our model on six held-out trials and compare it against signals' trajectories learned from training runs. Across all tests, the sensing output signals follow the training manifold, and the predicted sequence mirrors real performance and confirms repeatability. Despite deliberate train-test mismatches in actuation speed, the signal trajectories preserve their shape, and classification remains consistently accurate, demonstrating practical voltage- and speed-invariance. As a result, OS-ERA classifies bending states with high fidelity; it is fast and repeatable, solving a longstanding bottleneck of the ERA, enabling steps toward closed-loop control.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.17474v1</id>\n <title>Optically Sensorized Electro-Ribbon Actuator (OS-ERA)</title>\n <updated>2026-02-19T15:38:22Z</updated>\n <link href='https://arxiv.org/abs/2602.17474v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.17474v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Electro-Ribbon Actuators (ERAs) are lightweight flexural actuators that exhibit ultrahigh displacement and fast movement. However, their embedded sensing relies on capacitive sensors with limited precision, which hinders accurate control. We introduce OS-ERA, an optically sensorized ERA that yields reliable proprioceptive information, and we focus on the design and integration of a sensing solution without affecting actuation. To analyse the complex curvature of an ERA in motion, we design and embed two soft optical waveguide sensors. A classifier is trained to map the sensing signals in order to distinguish eight bending states. We validate our model on six held-out trials and compare it against signals' trajectories learned from training runs. Across all tests, the sensing output signals follow the training manifold, and the predicted sequence mirrors real performance and confirms repeatability. Despite deliberate train-test mismatches in actuation speed, the signal trajectories preserve their shape, and classification remains consistently accurate, demonstrating practical voltage- and speed-invariance. As a result, OS-ERA classifies bending states with high fidelity; it is fast and repeatable, solving a longstanding bottleneck of the ERA, enabling steps toward closed-loop control.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <published>2026-02-19T15:38:22Z</published>\n <arxiv:comment>6 pages, 5 figures, accepted for 9th IEEE-RAS International Conference on Soft Robotics (RoboSoft 2026)</arxiv:comment>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Carolina Gay</name>\n </author>\n <author>\n <name>Petr Trunin</name>\n </author>\n <author>\n <name>Diana Cafiso</name>\n </author>\n <author>\n <name>Yuejun Xu</name>\n </author>\n <author>\n <name>Majid Taghavi</name>\n </author>\n <author>\n <name>Lucia Beccai</name>\n </author>\n </entry>"
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