arXiv subjects

Skylar Tibbits

Publications and source records attributed to Skylar Tibbits.

5 recordsLinked to original sources

arXiv2026

Context Aware AI Assistant and AR Interface for Lunar Extravehicular Activity (EVA) Procedural Guidance

As human space exploration returns to the Moon, astronauts need rapid access to procedural information during extravehicular activities (EVAs), where attention is divided across navigation, repair tasks, tool handling, and environmental risk. The challenge is not the absence of information, but surfacing the right information at the right moment. We present GAIN-AI (Guided Assistant for Intelligent Navigation), a context-aware AI assistant and minimal heads-up interface for procedural guidance in simulated lunar EVA. The system operates in two layers. The first grounds a large language model with structured context: EVA procedure documents, live telemetry data, and error-handling protocols encoded as JSON. The second restructures that output into three compact units for AR display: Goal, Task, and Verification. Evaluated on 111 synthetic EVA scenarios, the system scores 10.0/10 on nominal conditions and 8.15/10 on single-fault scenarios, with performance degrading on multi-fault and boundary-threshold cases.

cs.HC↗

arXiv2026

A Category-Theoretic Framework from Biological Mechanics to Engineered Stimulus-Response Systems

Natural materials achieve adaptive behavior through hierarchical organization and coupled mechanisms across scales. Their translation into engineering, however, remains largely heuristic. What is missing is a formal translation framework that carries biological design logic into engineered realization while preserving physical consistency across levels of abstraction. Here we present a category theoretic compositional framework for verified nature-derived design. The framework defines a category of stimulus response dynamical systems with natural and artificial subcategories. It introduces a structure preserving implementation functor from biological mechanics to engineered systems. It also formalizes a machine agnostic specification layer that links behavioral intent to executable fabrication programs. We instantiate the framework on the hygromorphic pinecone hierarchy as a representative biological case. We implement the full pipeline in Grasshopper, where formal specifications are translated into modular parametric scripts that preserve the compositional structure of the model. The resulting designs are fabricated by fused filament fabrication, evaluated experimentally, and tested against model predictions derived from the pipeline. The current implementation generates four actuator classes spanning two stimulus types and two kinematic responses. One actuator arises purely through composition from previously validated components, without additional manual derivation. The results show that compositionality can function not just as a descriptive language, but as a generative and system level verifiable method for mechanical material design. More broadly, the work provides a concrete route for embedding formal multiscale reasoning within increasingly computational, generative, and physics-driven design workflows.

cond-mat.soft↗

arXiv2025

Utilizing a Geospatial Foundation Model for Coastline Delineation in Small Sandy Islands

We present an initial evaluation of NASA and IBM's Prithvi-EO-2.0 geospatial foundation model on shoreline delineation of small sandy islands using satellite images. We curated and labeled a dataset of 225 multispectral images of two Maldivian islands, which we publicly release, and fine-tuned both the 300M and 600M parameter versions of Prithvi on training subsets ranging from 5 to 181 images. Our experiments show that even with as few as 5 training images, the models achieve high performance (F1 of 0.94, IoU of 0.79). Our results demonstrate the strong transfer learning capability of Prithvi, underscoring the potential of such models to support coastal monitoring in data-poor regions.

cs.CV↗

arXiv2025

Multi-level mechanical modeling and computational design framework for weft knitted fabrics

This work presents a multi-level modeling and design framework for weft knitted fabrics, beginning with a volumetric finite element analysis capturing their mechanical behavior from fundamental principles. Incorporating yarn-level data, it accurately predicts stress-strain responses, reducing the need for extensive physical testing. A simplified strain energy approach homogenizes the results into three key variables, enabling rapid, accurate predictions in minutes. After validation against experiments, our framework can simulate new knit fabrics without additional tests. In real-world scenarios, fabrics often feature variations in yarn materials or patterns. The framework extends to heterogeneous fabrics, showing that transitions between distinct regions can be captured using simple mechanical analogies: springs in series and parallel. This allows heterogeneous textiles to be treated as idealized patchworks of homogeneous pieces, preserving predictive accuracy. The method is demonstrated by designing and producing a compression sleeve with uniform pressure, illustrating how the framework supports development of knits tailored to specific assistance levels and anatomical features. By combining volumetric finite element analysis, simplified model through homogenization, and controlled material transitions, this approach provides a scalable, high-fidelity path toward next-generation weft knitted fabric design.

cond-mat.soft↗

arXiv2025

Architected Dual-Network Solvent-free Adhesives for Stretchable Fabrics

Natural systems, such as tendons and spider silk, demonstrate how the combination of strength and stretchability can be effectively achieved by integrating stiff and flexible network structures. Inspired by these systems, we developed a novel, solvent-free dual-network adhesive based on a self-assembling ABA triblock copolymer, poly(methyl methacrylate)-poly(n-butyl acrylate)-poly(methyl methacrylate) (PMMA-b-PnBA-b-PMMA), designed for applications requiring both high strength and stretchability. The triblock copolymer forms a physically crosslinked network through microdomains of PMMA end-blocks that provide structural integrity, while the PnBA mid-block forms a soft, stretchable matrix. To further enhance mechanical performance, a second poly(n-butyl acrylate) (PnBA) network is polymerized in situ, locking the PMMA microdomains in place and creating a load-bearing system. By varying the crosslinking density of the secondary network, we tailor the adhesive's mechanical properties (Young's modulus: 0.17 - 1.18 MPa) to suit different substrates, creating a mechanically transparent seam. The resulting dual-network system combines different strategies to achieve high strength and stretchability, with adhesive performance comparable to industrial methods such as sewing, particularly in bonding neoprene fabric composites and sealing the joints. Our solvent-free approach also eliminates the need for lengthy solvent evaporation steps, offering an eco-friendly and more efficient alternative for flexible adhesive applications in fields such as soft robotics, flexible electronics, and sports apparel.

cond-mat.mtrl-sci↗