Optically Sensorized Electro-Ribbon Actuator (OS-ERA)

2026-02-19

AI Summary

This work addresses the challenge of achieving precise closed-loop control in Electro-Ribbon actuators, which has been hindered by the lack of high-fidelity proprioception. For the first time, a flexible optical waveguide sensor is integrated directly into the actuator, enabling voltage- and speed-independent, high-accuracy sensing of bending states without compromising actuation performance. By combining signal trajectory modeling with a machine learning classifier, the system reliably and accurately distinguishes among eight distinct bending configurations. Experimental validation demonstrates excellent repeatability and invariance across diverse operating conditions, surpassing the precision limitations of conventional capacitive sensing approaches. This integration establishes a novel paradigm for proprioceptive feedback in soft actuators, paving the way for robust closed-loop control in soft robotic systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Active Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Electro-Ribbon Actuator
• embedded sensing
• proprioception
• actuation control
• sensor precision

Innovation

• optical waveguide sensing
• electro-ribbon actuator
• proprioceptive feedback
• soft robotics
• closed-loop control