About

Ryan Truby is an Assistant Professor of Materials Science and Engineering and Mechanical Engineering at Northwestern University. His research is inspired by the performance gap between biological and artificial machines, with a broad aim to advance machine intelligence through material design. He leads the Robotic Matter Lab, which focuses on developing material systems that enable soft devices and robots with bioinspired actuation, perception, control, and power capabilities. His interdisciplinary approach addresses key challenges in the design, fabrication, and control of autonomous soft robots and robotic materials, specializing in the synthesis and characterization of soft, polymeric, and nanoscale materials, as well as innovative manufacturing methods, soft robot design, and rheological characterization of soft matter. His work includes developing soft artificial muscles and sensors, rapid multi-material fabrication techniques, and machine learning-based control strategies for soft robotics. Truby's contributions aim to pioneer autonomous systems that impact healthcare, environmental stewardship, exploration, and automation.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Control engineering
• Mechanical engineering
• Materials science
• Aerospace engineering
• Nanotechnology
• Embedded system

Selected publications

• Architected Materials for Soft Robotics

  Journal of materials research/Pratt's guide to venture capital sources · 2026-01-20

  articleOpen accessSenior author

  Abstract This perspective is based on a talk titled, “Soft Architected Robots: Printing Complex Forms for New Sensorimotor Capabilities in Robotics,” presented at the Fall 2024 Meeting of the Materials Research Society as part of the “Distinguished Invited Speaker” series. We highlight the latest progress in developing architected materials—namely kirigami, origami, auxetic structures, and lattices—for soft robots. In particular, we focus on recent examples of using soft, architected materials for soft robotic actuators, sensors, and sensorized soft actuators with embedded sensing capabilities. We provide an outlook on emerging opportunities in the use, design, and manufacturing of architected materials to advance the capabilities and practical performance of soft robots. We encourage the field to see this class of materials as essential to advancing robot capabilities more broadly beyond those afforded by traditional means and mechanisms. Graphical abstract

  PublisherOA PDFDOI

• Direct-Write Printing of Multifunctional Iontronic Composites That Sense, Rectify, and Actuate

  ACS Nano · 2026-01-03

  articleOpen accessSenior authorCorresponding

  The development of advanced materials capable of performing multiple functions is a key step toward adaptive, autonomous systems for emerging technologies. However, multifunctional material systems designed to integrate sensory, computational, and actuation capabilities are challenging to realize due to manufacturing and materials limitations. Here, we present electrically controllable, multifunctional iontronic composites (MICs) that demonstrate ionic sensing, current regulation, and ionomotive bending actuation capabilities within a single architecture. Our MICs are fabricated using a multimaterial direct-write printing process, in which a poly(ionic liquid) (pIL) structural electrolyte is sandwiched between two Ti3C2Tx MXene-based electrodes. The printing process enables seamless integration of concentrated MXene electrode inks with pIL electrolytes with printable layer thicknesses down to 25 and 200 μm, respectively. When used as a sensor, MICs exhibit capacitance changes up to 4% under compressive loads of 45 N. When printed with electrodes of asymmetric thickness, MICs can also function as ionic diodes, achieving rectification ratios up to 14. Finally, the composites demonstrate ionomotive actuation with a maximum bending strain of 0.21%. Our key innovation lies in achieving all three functionalities through additive manufacturing, which reduces the number of fabrication steps required to integrate all MIC materials together. Our MICs represent a significant advance in electrically controlled, multifunctional composites and motivate new directions toward next-generation autonomous and responsive material systems for soft robotics, electronics, and adaptive structures.

  PublisherOA PDFDOI

• Clutchable Soft Actuators Produce Rapid, High-Power Movements in Robotic Artificial Musculoskeletal Systems

  IEEE Robotics and Automation Letters · 2026-01-19

  articleOpen accessSenior author
  PublisherDOI

• Architected Soft Actuators for Artificial Musculoskeletal Systems

  Advanced Materials · 2025-07-24 · 7 citations

  articleOpen accessSenior authorCorresponding

  Vertebrates depend on their musculoskeletal system for locomotion, manipulation, interaction with their environment, and more. The robustness and efficiency of animal locomotion are difficult to achieve in robots because their hardware does not replicate the mechanics and performance of animal bodies. Moreover, many state-of-the-art soft actuators are ill-suited as muscles in artificial musculoskeletal systems for deployable, task-capable robots. This study presents an electrically-driven, architected soft actuator that can be assembled into artificial musculoskeletal systems. The fully 3D printed actuators linearly extend and contract through the rotation of an integrated servo motor. They comprise a thermoplastic polyurethane handed shearing auxetic (HSA) and origami bellows structure. Together, these structures transmit torque, stretch, and resist torsional deflection in a manner that produces large linear actuation and force output up to 59 mm (or 30% strain) and 75 N, respectively. It showcases the actuator's performance as artificial muscles in a battery-powered, human-scale leg that can use three muscles to kick a ball. When accounting for the weight of auxiliary hardware, the actuators exhibit power and energy densities that are four orders of magnitude higher than for leading soft artificial muscles. The soft actuators represent a step toward providing robots with bioinspired musculoskeletal systems for animal-like abilities.

  PublisherOA PDFDOI

• A Swinging, Variable-Length Soft Tail from 3D Printed Origami: Steps Toward Bioinspired Robot Walking

  2025-04-22 · 1 citations

  articleSenior author

  Tails are flexible appendages that many vertebrates use for balance, gait stabilization, thrust generation, and more. While robots rarely have them, soft walking robots may benefit from a tail that stabilizes locomotion in unstructured terrains. We present a tendon-driven, soft robotic tail capable of swinging and shortening to change the momentum and center of mass of a robot body. The tail comprises three origami bellows fully 3D printed from thermoplastic polyurethane. The bellows are highly compressible, allowing motorized tendon actuators to shorten the tail by 110 mm, or 30% of the tail’s initial length. The tail’s flexibility also enables large swinging motions, which are achieved by alternatively pulling and releasing two tendons routing at the sides of the bellows structures. Since the tail’s servo motors are at its end, the tail can generate up to 0.5 N•m torque while swinging. In this paper, we characterize the tail’s range of adjustable lengths, the swinging motions it can produce, and the torque it generates. Swinging and torque generation are evaluated at several different initial tail lengths. Finally, we demonstrate the tail’s controllability through a closed proportional-integral-derivative (PID) feedback controller. This work sets in motion future investigations of how vertebrate-inspired tails can enhance the mobility and stability of (soft) robot walking.

  PublisherDOI

• Miniaturized and Motorized: Fast, Architected Soft Robotic Actuators via Molded Thermoplastic Elastomers

  2025-04-22 · 2 citations

  articleSenior author

  Handed shearing auxetics, or HSAs, are a class of architected materials increasingly used as electrically-driven soft actuators. HSAs are directly driven by servo motors, resulting in architected soft robotic actuators that enable capabilities spanning manipulation and locomotion. However, the material properties and form factors available to HSAs are limited. Thus, fabricating miniaturized HSAs from robust, durable materials is difficult. Moreover, scaling HSAs to smaller form factors is also complicated by the need to miniaturize the motors driving them. Here, we present a method for fabricating miniaturized, robust HSA actuators via molding from thermoplastic polyurethane (TPU) powders. Our method produces soft HSA actuators with low torque requirements that can be actuated with DC micromotors. We describe the overall fabrication process for our actuators, characterize the free displacement and blocked force generated by single HSAs, and demonstrate the performance of a multi-DoF platform comprising a 2x2 assembly of HSAs. We find that our new HSAs produce actuation strains and forces of 40% and 1.2N, respectively, with servo motors; with DC micromotors, they can actuate to at least 20Hz. Altogether, the use of micromotors and thermoplastic elastomers enables us to achieve extremely robust and fast actuation with HSAs. We expect our new approach to HSA design, fabrication, and actuation will open up new opportunities in the use of architected soft robotic actuators that operate with the actuation bandwidths found in both rigid robots and living organisms.

  PublisherDOI

• Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot

  2025-10-19

  article

  Closed-loop control remains an open challenge in soft robotics. The nonlinear responses of soft actuators under dynamic loading conditions limit the use of analytic models for soft robot control. Traditional methods of controlling soft robots underutilize their configuration spaces to avoid nonlinearity, hysteresis, large deformations, and the risk of actuator damage. Furthermore, episodic data-driven control approaches such as reinforcement learning (RL) are traditionally limited by sample efficiency and inconsistency across initializations. In this work, we demonstrate RL for reliably learning control policies for dynamic balancing tasks in real-time single-shot hardware deployments. We use a deformable Stewart platform constructed using parallel, 3D-printed soft actuators based on motorized handed shearing auxetic (HSA) structures. By introducing a curriculum learning approach based on expanding neighborhoods of a known equilibrium, we achieve reliable single-deployment balancing at arbitrary coordinates. In addition to benchmarking the performance of model-based and model-free methods, we demonstrate that in a single deployment, Maximum Diffusion RL is capable of learning dynamic balancing after half of the actuators are effectively disabled, by inducing buckling and by breaking actuators with bolt cutters. Training occurs with no prior data, in as fast as 15 minutes, with performance nearly identical to the fully-intact platform. Single-shot learning on hardware facilitates soft robotic systems reliably learning in the real world and will enable more diverse and capable soft robots.

  PublisherDOI

• Autonomous codesign and fabrication of multistimuli-responsive material systems

  Science Advances · 2025-09-12 · 8 citations

  articleOpen access

  Responsive materials offer solutions to complex engineering challenges by enabling systems to adapt their shapes or properties in response to external stimuli. To fully harness the potential of responsive materials, inverse design methods that integrate multiple types of stimuli and manufacturing processes are necessary. We present a unified, autonomous codesign framework that simultaneously optimizes structure, manufacturing, materials, and stimuli for responsive material systems, achieving target shape morphing under multiple stimuli without relying on human heuristics or expertise. It integrates generalized topology optimization with hybrid data-physics differentiable simulations to achieve flexible, manufacturing-aware designs for network-like responsive material systems. We showcase our framework with a multimaterial three-dimensional printing process with high material tunability, which we use to fabricate liquid crystal elastomer systems that morph into different forms in response to heat and light. The exceptional flexibility and efficiency of our method will advance shape-morphing applications spanning soft robotics to drug delivery.

  PublisherDOI

• Architected Soft Actuators for Artificial Musculoskeletal Systems (Adv. Mater. 43/2025)

  Advanced Materials · 2025-10-01

  articleOpen accessSenior author

  Soft Actuators In their Research Article (DOI: 10.1002/adma.202501290), Ryan L. Truby and co-workers construct architected soft actuators from a combination of 3D printed elastomers that extend and contract upon rotation of integrated servo motors. The actuators exhibit high actuation stroke, force output, and power density. The authors construct human-scale legs with bone-inspired links, tendons, and three architected soft actuators. Image credit: T. Kim, R. L. Truby, Northwestern University.

  PublisherOA PDFDOI

• Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot

  ArXiv.org · 2025-09-23

  preprintOpen access

  Closed-loop control remains an open challenge in soft robotics. The nonlinear responses of soft actuators under dynamic loading conditions limit the use of analytic models for soft robot control. Traditional methods of controlling soft robots underutilize their configuration spaces to avoid nonlinearity, hysteresis, large deformations, and the risk of actuator damage. Furthermore, episodic data-driven control approaches such as reinforcement learning (RL) are traditionally limited by sample efficiency and inconsistency across initializations. In this work, we demonstrate RL for reliably learning control policies for dynamic balancing tasks in real-time single-shot hardware deployments. We use a deformable Stewart platform constructed using parallel, 3D-printed soft actuators based on motorized handed shearing auxetic (HSA) structures. By introducing a curriculum learning approach based on expanding neighborhoods of a known equilibrium, we achieve reliable single-deployment balancing at arbitrary coordinates. In addition to benchmarking the performance of model-based and model-free methods, we demonstrate that in a single deployment, Maximum Diffusion RL is capable of learning dynamic balancing after half of the actuators are effectively disabled, by inducing buckling and by breaking actuators with bolt cutters. Training occurs with no prior data, in as fast as 15 minutes, with performance nearly identical to the fully-intact platform. Single-shot learning on hardware facilitates soft robotic systems reliably learning in the real world and will enable more diverse and capable soft robots.

  PublisherOA PDFDOI

Frequent coauthors

• Jennifer A. Lewis

  23 shared

• Daniela Rus

  11 shared

• Pranav Kaarthik

  Northwestern University

  11 shared

• Sebastien G. M. Uzel

  Harvard University

  10 shared

• Abigail K. Grosskopf

  Stanford University

  9 shared

• Lillian Chin

  Massachusetts Institute of Technology

  7 shared

• Kimberly A. Homan

  Harvard University

  7 shared

• Robert J. Wood

  Harvard University

  7 shared

Education

• Ph.D., Applied Physics - Materials Science

  Harvard University

  2018

• B.S., Biomedical Engineering

  The University of Texas at Austin

  2012

Awards & honors

• DARPA Director's Fellowship (2025)
• Schmidt Science Fellows Community Leadership Award (2025)
• DARPA Young Faculty Award (DARPA YFA, 2023)
• Office of Naval Research Young Investigator Award (ONR YIP,…
• Air Force Office of Scientific Research Young Investigator A…