Additive manufacturing is enabling soft robots with increasingly complex geometries, creating a demand for sensing solutions that remain compatible with single-material, one-step fabrication. Optical soft sensors are attractive for monolithic printing, but their performance is often degraded by uncontrolled light propagation (ambient coupling, leakage, scattering), while common miti- gation strategies typically require multimaterial interfaces. Here, we present an approach for 3D printed soft optical sensing (SOLen), in which a printed lens is placed in front of an emitter within a Y-shaped waveguide. The sensing mechanism relies on deformation-induced lens rotation and focal-spot translation, redistributing optical power between the two branches to generate a differential output that encodes both motion direction and amplitude. An acrylate polyurethane resin was modified with lauryl acrylate to improve compliance and optical transmittance, and single-layer optical characterization was used to derive wavelength-dependent refractive index and transmittance while minimizing DLP layer-related artifacts. The measured refractive index was used in simulations to design a lens profile for a target focal distance, which was then printed with sub-millimeter fidelity. Rotational tests demonstrated reproducible branch-selective signal switching over multiple cycles. These results establish a transferable material-to-optics workflow for soft optical sensors with lens with new functionalities for next-generation soft robots
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Figure 1: (A) Acrylate soft polyurethane was 3D printed via UV-based DLP to fabricate (B) SOLen sensor, with post-assembled emitter and photoreceiver. (C) Microscope image of the 3D printed lens, which is the basis of the (D) working principle of the SOLen. On the left, the sensor in the undeformed state, on the right, the sensor under rotation. The focal spot point moves with the lens, guiding the light.
Figure 2: UV-Vis spectra of (A) Transmittance T and (B) Absorbance A of one-layer of ma-PU (C) the analytical refractive index, measured by modeling the printed layer as a slab of thickness h. λ is the wavelength of the incident light, and R is the reflectance.
Figure 3: (A). Graph three different lens profiles obtained with different RI parameters in the Cartesian oval’s equation. (B). Zoom on the lens (RI=1.44) during the simulation. (C). Simulation after the lens rotation of −3∘ . (D). Simulation after the lens rotation of +3∘ . (E-G) Printed lens microscope images compared with the Cartesian ovals for the different refractive index (RI in the figure).
Figure 4: Figure 4. Experimental validation of lens-driven focal switching in the Y-shaped optical sensor. (A) Initial configuration of the test, where the emitter is aligned with the centerline between the two branches, placing the focal spot between ReceiverL and ReceiverR. (B) Right-rotation state (-3°), achieved by tilting the emitter/lens assembly to shift the focal spot into the right branch toward ReceiverR. (C) Left-rotation state (+3°), where the focal spot is redirected into the left branch toward ReceiverL. (D) Measured receiver signals plotted as voltage versus rotation angle for three sensors equipped with lenses over five consecutive cycles (mean curve with shaded standard deviation). A clear intensity redistribution is observed between the two receivers: during right rotation, ReceiverR shows a voltage decrease (higher intensity) while ReceiverL shows a voltage increase (lower intensity), with the opposite trend during left rotation.
Figure 5: Conceptual example of SOLen integration in a monolithic soft robot architecture. Selected structural elements act as waveguides, while printed lenses focus and steer light toward predefined directions to enable distributed sensing.
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
We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surface deformation of silicone skin to estimate contact force without embedding tactile transducers. Locating the sensor off the contact interface reduces damage risk, preserves softness, and simplifies fabrication and maintenance. We first characterize the optical sensing element and the compliant skin, thendetermine the design of a prototype tactile sensor. Compression experiments validate the approach, exhibiting a monotonic force output relationship consistent with theory, low hysteresis, high repeatability over repeated cycles, and small response indentation speeds. We further demonstrate integration on a soft robotic gripper, where the module reliably detects grasp events. Compared with liquid filled or wireembedded tactile skins, the proposed modular add on architecture enhances durability, reduces wiring complexity, and supports straightforward deployment across diverse robot geometries. Because the sensing principle reads skin strain patterns, it also suggests extensions to other somatosensory cues such as joint angle or actuator state estimation from surface deformation. Overall, leveraging surface compliance with an external optical module provides a practical and robust route to equip soft robots with force perception while preserving structural flexibility and manufacturability, paving the way for robotic applications and safe human robot collaboration.
Soft robots enable safe, adaptive interaction with complex environments but remain difficult to sense and control due to their highly deformable structures. Architected soft materials such as helicoid lattices offer tunable stiffness and strength but are challenging to instrument because of their sparse geometry. We introduce a fabrication method for embedding air channels into helicoid-based soft continuum robots. Multi-material segments fabricated via vision-controlled jetting in a single print interface with PCBs housing miniature pressure sensors and IMUs for distributed deformation sensing. We characterize the mechanical properties of four helicoid designs and validate the sensor response to fundamental deformation modes. To demonstrate the platform's scalability, we construct and mechanically evaluate a meter-scale, 14-DoF cable-driven soft arm capable of open-loop trajectory tracking and object grasping, with tactile-based stiffness detection demonstrated using the gripper sensors. This approach establishes a scalable fabrication strategy for sensorized architected materials in large-scale soft robotic systems.
Annan Zhang, Hanna Matusik, Miguel Flores-Acton +3
All authors are with the MIT Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA.