arXiv subjects

Hannah Stuart

Publications and source records attributed to Hannah Stuart.

3 recordsLinked to original sources

arXiv2026

Interdisciplinary Workshop on Mechanical Intelligence: Summary Report

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

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arXiv2025

Characterizing the Resilience and Sensitivity of Polyurethane Vision-Based Tactile Sensors

Vision-based tactile sensors (VBTSs) are a promising technology for robots, providing them with dense signals that can be translated into a multi-faceted understanding of contact. However, existing VBTS tactile surfaces make use of silicone gels, which provide high sensitivity but easily deteriorate from loading and surface wear. We propose that polyurethane rubber, a typically harder material used for high-load applications like shoe soles, rubber wheels, and industrial gaskets, may provide improved physical gel resilience, potentially at the cost of sensitivity. To compare the resilience and sensitivity of two polyurethane gel formulations against a common silicone baseline, we propose a series of repeatable characterization protocols. Our resilience tests assess sensor durability across normal loading, shear loading, and abrasion. For sensitivity, we introduce learning-free assessments of force and spatial sensitivity to directly measure the physical capabilities of each gel without effects introduced from data and model quality. We also include a bottle cap loosening and tightening demonstration to validate the results of our controlled tests with a real-world example. Our results show that polyurethane yields a more robust sensor. While it sacrifices sensitivity at low forces, the effective force range is largely increased, revealing the utility of polyurethane VBTSs over silicone versions in more rugged, high-load applications.

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arXiv2023

Nonlinear Modeling for Soft Pneumatic Actuators via Data-Driven Parameter Estimation

Precise modeling soft robots remains a challenge due to their infinite-dimensional nature governed by partial differential equations. This paper introduces an innovative approach for modeling soft pneumatic actuators, employing a nonlinear framework through data-driven parameter estimation. The research begins by introducing Ludwick's Law, providing a accurate representation of the large deflections exhibited by soft materials. Three key material properties, namely Young's modulus, tensile stress, and mixed viscosity, are utilized to estimate the parameters inside the nonlinear model using the least squares method. Subsequently, a nonlinear dynamic model for soft actuators is constructed by applying Ludwick's Law. To validate the accuracy and effectiveness of the proposed method, several experiments are performed demonstrating the model's capabilities in predicting the dynamic behavior of soft pneumatic actuators. In conclusion, this work contributes to the advancement of soft pneumatic actuator modeling that represents their nonlinear behavior.

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