Printed helicoids with embedded air channels make sensorized segments for soft continuum robots

Annan Zhang; Hanna Matusik; Miguel Flores-Acton; Emily R. Sologuren; Joshua Jacob; Daniela Rus

cs.ROFeb 26, 2026

Printed helicoids with embedded air channels make sensorized segments for soft continuum robots

Authors: Annan Zhang, Hanna Matusik, Miguel Flores-Acton, Emily R. Sologuren, Joshua Jacob, Daniela Rus

Organizations: All authors are with the MIT Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA.

Abstract

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.

Figures & tables

Fig. 1 : Overview of the multi-material soft continuum robot made of helicoid segments with embedded air channels.

Fig. 2 : Helicoid construction showing annular sector parameters, helical sweep with axial rise and twist, and repeated structure.

Parameter	N4	N4T	N6	N8
Helicoid number, $N$	4	4	6	8
Sector angle, $\theta$ ( ∘ )	18	24	12	9
Twist angle, $\phi$ ( ∘ )	45	45	30	22.5
Strut thickness at $r_{i}$ , $t(r_{i})$ (mm)	3.04	4.05	2.60	2.22
Strut thickness at $r_{o}$ , $t(r_{o})$ (mm)	3.36	4.48	3.12	2.86

TABLE I : Four helicoid designs under investigation

Fig. 3 : Comparison of the four helicoid designs showing the variation in strut thickness and spacing.

Fig. 4 : Left: N6 segment with rigid end plates and air channels. Right: Transparent view showing air channel routing through the helicoid structure.

Fig. 5 : Left: Custom PCB with integrated pressure sensors and IMU. Right: CAN-USB adapter for interfacing with a host computer.

Fig. 6 : Mechanical characterization results. Top: axial stiffness and Bottom: bending stiffness for all four helicoid designs (N4, N4T, N6, N8) compared to theoretical predictions.

Fig. 7 : Sensor response characterization: pressure and IMU readings from the embedded air channels as the segment is subjected to different loading conditions.

Fig. 8 : Left: Full arm assembly. Top Right: Exploded view of motor base assembly showing motor arrangement, cable guide plates, and mounting plates. Bottom Right: Transparent view of gripper revealing the embedded air channels.

Fig. 9 : Trajectory tracking results for three different paths. Left column: 3D task space trajectories showing commanded (black dashed) vs. executed (red solid) paths reconstructed from motor encoders via forward kinematics. Right column: Velocity tracking for the three motors, with performance metrics (RMSE and phase lag) shown above each plot.

Fig. 10 : Object grasping. Top: The gripper successfully adapts to objects of various shapes, sizes, and stiffnesses. Bottom: Sequential images showing the robot picking up an apple from a pedestal and placing it back down.

Fig. 11 : Object stiffness detection: Average pressure in the air channels as the robot is in contact with different objects. Error bars show standard deviation.

Soft Robotics

Soft robotics studies robots with compliant bodies and actuators that deform to move, sense, and interact with uncertain environments.

See all 231 papers

A Model-Based Decoupling Strategy for Proprioception and Contact Sensing in an Architected Soft Manipulator
Jul 17, 2026Francesco Stella, Annan Zhang, Cosimo Della Santina +2Tactile Sensing for Robotic Manipulation Soft Robotics
Embodied AI SA, Lausanne, Switzerland · CREATE Lab, STI, EPFL, CH-1015 Lausanne, Switzerland · MIT Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA +2
Cosserat Modeling of Trimmed Helicoid Soft Arms with a Separated-Section Constitutive Law
Sep 21, 2026Zhihang Qin, Linxin Hou, Zeyu Zhong +10Structural Mechanics Continuum Robot Control
Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore, Singapore · Advanced Robotics Centre, National University of Singapore, Singapore · Department of Electrical and Computer Engineering, National University of Singapore, Singapore +1
Ori-Sense: origami capacitive sensing for soft robotic applications
Feb 20, 2026Hugo de Souza Oliveira, Xin Li, Mohsen Jafarpour +1Soft Robotics Robotic Perception

Jul 17, 2026J/K move · Enter open · S save

A Model-Based Decoupling Strategy for Proprioception and Contact Sensing in an Architected Soft Manipulator

Francesco Stella, Annan Zhang, Cosimo Della Santina, Josie Hughes+1

Embodied AI SA, Lausanne, Switzerland · CREATE Lab, STI, EPFL, CH-1015 Lausanne, Switzerland · MIT Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA+2

Tactile Sensing for Robotic Manipulation Soft Robotics Robotic Perception

Open paper AskPDF arXiv

Soft continuum robots require embedded sensing for proprioception and contact detection, yet integrating sensors into sparse, highly deformable architected structures remains challenging. We present a model-based strategy that decouples proprioceptive and contact signals from a common set of fluidic pressure sensors embedded in a soft architected segment. Each segment of the Innervated Trimmed Helicoid (ITH) contains six air channels routed in a localized zigzag pattern along the circumference. With only three principal kinematic degrees of freedom (axial compression, bending in x, bending in y), the six pressure readings form an overdetermined system. A piecewise constant curvature model maps pressures to shape, and Huber regression identifies outlier channels whose residuals indicate external contact. On a single ITH segment, this approach achieves proprioceptive shape estimation with a relative bending error of 0.11 +/- 0.02 and a contact detection rate of 97% across 178 trials. We integrate eight ITH segments into Air-Helix, a tendon-driven soft continuum manipulator, and present exploratory whole-arm demonstrations that include tactile teaching by demonstration, admittance-controlled force regulation, and tactile object reconstruction. The results suggest that localized fluidic innervation combined with model-based redundancy resolution is a practical path toward concurrent proprioception and contact sensing in architected soft robots.

Abstract

Soft Robotics

Explore similar work

A Model-Based Decoupling Strategy for Proprioception and Contact Sensing in an Architected Soft Manipulator

Cosserat Modeling of Trimmed Helicoid Soft Arms with a Separated-Section Constitutive Law

Ori-Sense: origami capacitive sensing for soft robotic applications