Stephanie Woodman

PhD Candidate at Yale University

Hello! I am a Ph.D. candidate in Mechanical Engineering and Materials Science at Yale University, studying under Prof. Rebecca Kramer-Bottiglio. As a NASA NSTGRO Fellow and NSF GRFP awardee, my research focuses on general shape-changing robots, stretchable computation, and model-free shape-sensing. My work has been featured in Science Robotics, IEEE RA-L, Advanced Intelligent Systems, Advanced Materials, and others, and I have been lucky enough to apply my work in industry at Meta Reality Labs, and in the government sector at NASA Ames Research Center. This year, I was honored to be invited to talk at the Max Plank Institute for Intelligent Systems, and be recognized as a Rising Star of the 2025 IEEE Conference on Soft Robotics.

Education

My education has led me to study soft robotics, and focus on achieving closed-loop shape control in fully soft and morphable robots using robot-agnostic shape sensing.

2020 - 2026

Ph.D., Mechanical Engineering, Yale University

At Yale University, I am working under Professor Rebecca Kramer-Bottiglio in the Faboratory studying sensing for shape-changing robots, and stretchable electronics.

2016 - 2020

B.S., Mechanical Engineering, Boston University

At Boston University, I worked in both the Morphable Biorobotics Lab under Professor Tommaso Ranzani, and the Mesoscale Soft Matter Lab under Professor Keith Brown. My work centered on enhancing the properties of magnetorheological fluids, and applying them to soft robots.

Workplace Skills

Comprehensive knowledge in soft robot and sensor design, fabrication, and evaluation, with specific knowledge in sensor fusion algorithms, ROS2, embedded programming, and custom PCB design.

ROS2 (Python 3)
C, Nordic embedded suite for BLE
SEM, XRD, EDS, materials testing

Soft robot design and manufacturing
Academic writing
Experimental design

Selected Projects

Stretchable Arduinos embedded in soft robots

Published Sept 2024 in Science Robotics

See the editor's summary below, and check out the Overview Video and an in-depth interview with Sparkfun Electronics!

Editor's summary: Commercial and open-source electronic circuit boards are widely used for controlling robots; however, their rigid form makes it difficult to embed them into soft robots. Here, Woodman et al. developed a generalized method for converting multilayered circuits into a soft, stretchable form. Commercial circuits were recreated by patterning biphasic liquid metal onto tacky films, and their continued operation was demonstrated during high-strain cycling. Using this method, stretchable Arduino Pro Minis were created and embedded into the body of a soft crawling robot to control locomotion, into pneumatic cubes for sensing and communicating, and into the high-strain elbow area of a wearable sleeve for tracking motion. —Melisa Yashinski

Read full article

Reference: Woodman, S. J., Shah, D. S., Landesberg, M., Agrawala, A., & Kramer-Bottiglio, R. (2024). Stretchable Arduinos embedded in soft robots. Science Robotics, 9(94). https://doi.org/10.1126/SCIROBOTICS.ADN6844

Sensing for general shape-changing robots

Motivating my current work

An ideal robot could autonomously complete diverse tasks such as surveying the ocean, cleaning the kitchen, and aerial environmental monitoring. However, robots optimized for each task typically have different shapes, posing a challenge in reconciling form and function. This challenge has inspired my pursuit of general shape-changing robots (GSCRs). While soft materials and actuators are promising for GSCRs due to their ability to accommodate extreme deformations, there is a gap between the vision of GSCRs and the simple examples we see today. Two critical components are needed: robot-agnostic stretchable shape sensing and stretchable computing. Together, these components would enable closed-loop shape control and the first instantiations of GSCRs. My work on stretchable electronics and shape sensing aims to bridge the gap between today's shape-changing robots and my envisioned GSCRs, ultimately guiding the field toward more versatile and adaptive robots.

Read full review article framing this work

Reference: Woodman, S. J., & Kramer-Bottiglio, R. (2024). Stretchable Shape Sensing and Computation for General Shape-Changing Robots. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/ANNUREV-CONTROL-030123-013355

My current work

Through my PhD, I have developed two robot-agnostic stretchable shape sensing ribbons that can reconstruct 2D shape. The working principle is that shape can be reconstructed along a surface if we can obtain orientations across that surface, and we know the distances between those orientations. With these two works, we have delved into the initial design and capability of this system.

Recently, I have also created a highly deformable 2D sheet that can sense 3D shape with high accuracy (<5%), across diverse shapes and in both static and dynamic conditions. This work is exciting, so stay tuned for its publication! Sneak peek video below.

Read Stretchable Shape-sensing Sheets and Stretchable Shape-sensing Ribbons

Reference 1: Shah, D., Woodman, S. J., Sanchez-Botero, L., Liu, S., & Kramer-Bottiglio, R. (2023). Stretchable Shape-Sensing Sheets. Advanced Intelligent Systems, 2300343. https://doi.org/10.1002/AISY.202300343

Reference 2: Woodman, S. J., Agrawala, A., & Kramer-Bottiglio, R. (2023). Stretchable Shape-Sensing Ribbons. Proceedings of IEEE Sensors. https://doi.org/10.1109/SENSORS56945.2023.10325035

Awards

NASA Space Technology Graduate Research Opportunity (NSTGRO) Fellowship (2022, awarded, accepted)
National Science Foundation Graduate Research Fellowship (NSF GRF) (2022, awarded, declined)
Startup Yale 2022 – Blavatnik Accelerator Award (2023)
Rising Star at IEEE RoboSoft 2025

Publications

Google Scholar

Woodman, S. J., Shah, D. S., Landesberg, M., Agrawala, A., & Kramer-Bottiglio, R. (2024). Stretchable Arduinos embedded in soft robots. Science Robotics, 9(94). https://doi.org/10.1126/SCIROBOTICS.ADN6844
Woodman, S. J., & Kramer-Bottiglio, R. (2024). Stretchable Shape Sensing and Computation for General Shape-Changing Robots. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/ANNUREV-CONTROL-030123-013355
Woodman, S. J., Agrawala, A., & Kramer-Bottiglio, R. (2023). Stretchable Shape-Sensing Ribbons. Proceedings of IEEE Sensors. https://doi.org/10.1109/SENSORS56945.2023.10325035
Shah, D., Woodman, S. J., Sanchez-Botero, L., Liu, S., & Kramer-Bottiglio, R. (2023). Stretchable Shape-Sensing Sheets. Advanced Intelligent Systems, 2300343. https://doi.org/10.1002/AISY.202300343
Woodman, S. J., Moore, A., Tagare, S., Cheung, K., & Kramer-Bottiglio, R. (2024). Deployable Cuboctahedrons for Adaptive Space Infrastructure. 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), 75–81. https://doi.org/10.1109/ROBOSOFT60065.2024.10521988
Yang, B., Nasab, A. M., Woodman, S. J., Thomas, E., Tilton, L. G., Levin, M., & Kramer-Bottiglio, R. (2024). Self-Amputating and Interfusing Machines. Advanced Materials, 2400241. https://doi.org/10.1002/ADMA.202400241
Shah, D. S., Woodman, S. J., Buckner, T. L., Yang, E. J., & Kramer-Bottiglio, R. K. (2024). Robotic Skins With Integrated Actuation, Sensing, and Variable Stiffness. IEEE Robotics and Automation Letters, 9(2), 1147–1154. https://doi.org/10.1109/LRA.2023.3337702
Johnson, W. R., Woodman, S. J., & Kramer-Bottiglio, R. (2022). An Electromagnetic Soft Robot that Carries its Own Magnet. 2022 IEEE 5th International Conference on Soft Robotics, RoboSoft 2022, 761–766. https://doi.org/10.1109/ROBOSOFT54090.2022.9762196
Pigozzi, F., Woodman, S., Medvet, E., Kramer-Bottiglio, R., & Bongard, J. (2023). Morphology Choice Affects the Evolution of Affordance Detection in Robots. GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference, 23, 211–219. https://doi.org/10.1145/3583131.3590505
Davis, Q. T., Woodman, S. J., Landesberg, M., Kramer‐Bottiglio, R., & Bongard, J. (2023). Subtract to adapt: Autotomic robots. IEEE Int. Conf. on Soft Robotics 2023. https://ieeexplore.ieee.org/abstract/document/10122102
Rendos, A., Li, R., Woodman, S., Ling, X., & Brown, K. A. (2021). Reinforcing Magnetorheological Fluids with Highly Anisotropic 2D Materials. ChemPhysChem, 22(5), 435–440. https://doi.org/10.1002/CPHC.202000948
McDonald, K., Rendos, A., Woodman, S., Brown, K. A., & Ranzani, T. (2020). Magnetorheological Fluid‐Based Flow Control for Soft Robots. Advanced Intelligent Systems, 2000139. https://doi.org/10.1002/aisy.202000139
Rendos, A., Woodman, S., McDonald, K., Ranzani, T., & Brown, K. A. (2020). Shear thickening prevents slip in magnetorheological fluids. Smart Materials and Structures, 29(7), 07LT02. https://doi.org/10.1088/1361-665X/ab8b2e
Case, J., Branin, G., Yang, E., Woodman, S. J., Gilbert, J., & Kramer-Bottiglio, R., (2025). Expanded quasistatic models to predict the performance of robotic skins on soft cylinders. International Journal of Robotics Research. https://journals.sagepub.com/doi/abs/10.1177/02783649241310885

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