Organizations: C. Gay, P. Trunin, D. Cafiso and L. Beccai are with the Soft BioRobotics and Perception Lab of the Istituto Italiano di Tecnologia, Via Morego 30 16163, Genova, Italy · Y. Xu and M. Taghavi are with the Department of Bioengineering, Imperial College London, SW7 2BX London, U.K.
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.
Figures & tables
Fig. 1: A. Optically Sensorized Electro-ribbon actuator during movement. B. Working principle of the bending optical sensor C. Working principle of ERA.
Fig. 2: A. Curvature of the two areas to sensorize of the upper electro-ribbon during the loading (20 g mass for 51.75 mm with a voltage of 8 kV). B. Design of the upper ribbon surface equipped with the soft optical sensors, S1 and S2. Two emitters are positioned in the clips near the anchoring point, while two receivers are placed in the distal clips.
Fig. 3: A. Fabrication process B. OS-ERA sample configuration C. Example of encapsulated component D. Insight image of the waveguide superficial structure
Fig. 4: A. Mean and standard deviation of the three training trials (12.3 g, 4 kV) for the two sensors (in black and in red). The colored dots are equally spaced in time (except for the last one) and represent the eight selected states during actuator contraction. The green band indicates the time interval during which the sensor buckles. B. Output of the SVM model showing the two sensor signals on the axes, the colored areas and decision boundaries for the eight classes, and the corresponding training samples (dots).
Fig. 5: A-H. Prediction example during OS-ERA movement (12.3 g, 5 kV). The bottom-left number indicates the predicted state, while the colored ribbon above the actuator represents the corresponding curve (saved from training video). The images show frames extracted from the video (12.3 g, 5 kV) at the moments of each prediction. I. Signals’ trajectories of all testing trials on the SVM model map. The three blue trajectories correspond to the testing trials performed at 3 kV, while the three red trajectories correspond to those performed at 5 kV. The colored dots represent the training data obtained from the three trials performed at 4 kV.
Dielectric elastomer actuators (DEAs) have garnered extensive attention especially in soft robotic applications over the past few decades owing to the advantages of lightweight, large strain, fast response and high energy density. However, because the DEAs suffer from nonlinear elasticity, inherent viscoelastic creep, hysteresis and vibrational dynamics, the modeling, control and self-sensing of DEAs are challenging, thereby hindering the practical applications of DEAs. In order to address these challenges, numerous studies have been conducted. In this review, various physics-based modeling methods and phenomenological modeling methods for predicting the electromechanical response of DEAs are presented and discussed. Different control methods for DEAs are reviewed, which are classified into open-loop feedforward control, feedback control, feedforward-feedback control and adaptive feedforward control. Physics-based self-sensing methods and data-driven self-sensing methods for reconstructing the DEA displacement without the need for additional sensors are discussed. Finally, the existing problems and new opportunities for the further studies are summarized.
Y. Zhao, G. Meng
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China · State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
This work introduces Ori-Sense, a compliant capacitive sensor inspired by the inverted Kresling origami pattern. The device translates torsional deformation into measurable capacitance changes, enabling proprioceptive feedback for soft robotic systems. Using dissolvable-core molding, we fabricated a monolithic silicone structure with embedded conductive TPU electrodes, forming an integrated soft capacitor. Mechanical characterization revealed low stiffness and minimal impedance, with torque values below 0.01 N mm for axial displacements between -15 mm and 15 mm, and up to 0.03 N mm at 30 degrees twist under compression. Finite-element simulations confirmed localized stresses along fold lines and validated the measured torque-rotation response. Electrical tests showed consistent capacitance modulation up to 30%, directly correlated with the twist angle, and maximal sensitivity of S_theta ~ 0.0067 pF/deg at 5 mm of axial deformation.
Hugo de Souza Oliveira, Xin Li, Mohsen Jafarpour +1
The growing adoption of lattice-based structures in soft robotics creates a need for advanced sensing solutions capable of monitoring their global deformation, particularly compression and extension. In this work, we address this challenge by introducing a novel optical sensor based on two patterned waveguides arranged in an ellipsoidal geometry. This Bidirectional Optical sensor for Actuation Tracking (BOAT) is seamlessly co-printed with a lattice structure actuated by an embedded pneumatic artificial muscle (PAM), and its performance is assessed. During PAM elongation or contraction, the bending of the embedded BOAT waveguides induces output signal variations that enable a clear discrimination between compression and extension states. The designs of both each specific waveguide structure (by surface patterning) and of the sensorized lattice-based unit embedding two BOATs are supported by numerical simulations. Experimental calibration over 100 consecutive pressure cycles ranging from +50 kPa to −40 kPa demonstrates a highly repeatable response, allowing a reliable distinction between extension and compression. Finally, sensor feedback is used to implement a digital shadow, enabling continuous synchronization between the whole sensorized unit and its virtual counterpart. These results establish BOAT as a powerful and reliable approach for deformation monitoring in soft lattice-based robotic systems.
Petr Trunin, Carolina Gay, Anderson Brazil Nardin +3