Hello! My primary research focuses on soft robotics and shape-changing robots. I obtained my BS in mechanical engineering from Boston University, and had the opportunity there to work in both the Morphable Biorobotics Lab and the Mesoscale Soft Matter Lab. Now working in the Faboratory at Yale University, funded by a NASA fellowship, I develop sensing technology for robots that change shape to adapt on demand.
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
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
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.
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
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
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
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
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
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)
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. (2022). Evolving Adaptive Autotomy in 2-Dimensional Simulated Soft Robots. Submitted to RoboSoft.
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