Francesco Stella
École Polytechnique Fédérale de Lausanne, Delft University of Technology
Papers
16
Total Citations
338
H-Index
10
About
Francesco Stella is a leading researcher at the intersection of soft robotics and artificial intelligence, whose work is fundamentally reshaping how we design, model, and control compliant robotic systems. His key contributions span soft robot design methodologies, proprioceptive sensing, and the transformative application of large language models (LLMs) to robotic design processes. Stella’s highly cited 2023 paper on using LLMs to transform robotic design (59 citations) has opened a new frontier in automated design, while his comprehensive review on the science of soft robot design (55 citations) has become a foundational reference for the field. He is perhaps best known for developing the trimmed helicoid architectured structure (48 citations), a novel topology that enables soft robots to achieve high precision, large workspace, and compliant interactions simultaneously—a breakthrough that addresses a long-standing trade-off in soft robotics. Stella has also made significant advances in state estimation and control, including drift-filtering methods using IMUs and provably stable iterative learning controllers for continuum soft robots. His work on polynomial curvature models and piecewise affine curvature models has provided the theoretical foundations for accurate, computationally tractable dynamic models of soft structures interacting with their environment. With over 300 total citations and multiple high-impact publications in 2023 alone, Stella is rapidly establishing himself as a pivotal figure in the next generation of soft robotics research.
Research Focus
Key Achievements
Top Papers
- 1How can LLMs transform the robotic design process?59 citations · 2023
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- 4Sensing Soft Robot Shape Using IMUs: An Experimental Investigation30 citations · 2021
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- 9A Provably Stable Iterative Learning Controller for Continuum Soft Robots14 citations · 2023
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