arXiv subjects

Lining Yao

Publications and source records attributed to Lining Yao.

12 recordsLinked to original sources

arXiv2026

DuoMorph: Synergistic Integration of FDM Printing and Pneumatic Actuation for Shape-Changing Interfaces

We introduce DuoMorph, a design and fabrication method that synergistically integrates Fused Deposition Modeling (FDM) printing and pneumatic actuation to create novel shape-changing interfaces. In DuoMorph, the printed structures and heat-sealed pneumatic elements are mutually designed to actuate and constrain each other, enabling functions that are difficult for either component to achieve in isolation. Moreover, the entire hybrid structure can be fabricated through a single, seamless process using only a standard FDM printer, including both heat-sealing and 3D and 4D printing. In this paper, we define a design space including four primitive categories that capture the fundamental ways in which printed and pneumatic components can interact. To support this process, we present a fabrication method and an accompanying design tool. Finally, we demonstrate the potential of DuoMorph through a series of example applications and performance demonstrations.

cs.HC↗

arXiv2025

RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward

Robot co-design, jointly optimizing morphology and control policy, remains a longstanding challenge in the robotics community, where many promising robots have been developed. However, a key limitation lies in its tendency to converge to sub-optimal designs due to the use of fixed reward functions, which fail to explore the diverse motion modes suitable for different morphologies. Here we propose RoboMoRe, a large language model (LLM)-driven framework that integrates morphology and reward shaping for co-optimization within the robot co-design loop. RoboMoRe performs a dual-stage optimization: in the coarse optimization stage, an LLM-based diversity reflection mechanism generates both diverse and high-quality morphology-reward pairs and efficiently explores their distribution. In the fine optimization stage, top candidates are iteratively refined through alternating LLM-guided reward and morphology gradient updates. RoboMoRe can optimize both efficient robot morphologies and their suited motion behaviors through reward shaping. Results demonstrate that without any task-specific prompting or predefined reward/morphology templates, RoboMoRe significantly outperforms human-engineered designs and competing methods across eight different tasks.

cs.RO↗

arXiv2024

Degrade to Function: Towards Eco-friendly Morphing Devices that Function Through Programmed Sequential Degradation

While it seems counterintuitive to think of degradation within an operating device as beneficial, one may argue that when rationally designed, the controlled breakdown of materials can be harnessed for specific functions. To apply this principle to the design of morphing devices, we introduce the concept of Degrade to Function (DtF). This concept aims to create eco-friendly and self-contained morphing devices that operate through a sequence of environmentally-triggered degradations. We explore its design considerations and implementation techniques by identifying environmental conditions and degradation types that can be exploited, evaluating potential materials capable of controlled degradation, suggesting designs for structures that can leverage degradation to achieve various transformations and functions, and developing sequential control approaches that integrate degradation triggers. To demonstrate the viability and versatility of this design strategy, we showcase several application examples across a range of environmental conditions.

cs.HC↗

arXiv2024

Roadmap for Animate Matter

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts across various sectors. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field encouraging cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.

cond-mat.mtrl-sci↗

arXiv2024

Enabling Generative Design Tools with LLM Agents for Mechanical Computation Devices: A Case Study

In the field of Human-Computer Interaction (HCI), interactive devices with embedded mechanical computation are gaining attention. The rise of these cutting-edge devices has created a need for specialized design tools that democratize the prototyping process. While current tools streamline prototyping through parametric design and simulation, they often come with a steep learning curve and may not fully support creative ideation. In this study, we use fluidic computation interfaces as a case study to explore how design tools for such devices can be augmented by Large Language Model agents (LLMs). Integrated with LLMs, the Generative Design Tool (GDT) better understands the capabilities and limitations of new technologies, proposes diverse and practical applications, and suggests designs that are technically and contextually appropriate. Additionally, it generates design parameters for visualizing results and producing fabrication-ready support files. This paper details the GDT's framework, implementation, and performance while addressing its potential and challenges.

cs.HC↗

arXiv2023

Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools

AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks. These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as "co-creators." Yet, working effectively with such systems requires different skills than working with complex CAD tools alone. To date, we know little about how engineering designers learn to work with AI-based design tools. In this study, we observed trained designers as they learned to work with two AI-based tools on a realistic design task. We find that designers face many challenges in learning to effectively co-create with current systems, including challenges in understanding and adjusting AI outputs and in communicating their design goals. Based on our findings, we highlight several design opportunities to better support designer-AI co-creation.

cs.HC↗

arXiv2023

Guttation Monitor: Wearable Guttation Sensor for Plant Condition Monitoring and Diagnosis

Plant life plays a critical role in the ecosystem. However, it is difficult for humans to perceive plants' reactions because the biopotential and biochemical responses are invisible to humans. Guttation droplets contain various chemicals which can reflect plant physiology and environmental conditions in real-time. Traditionally, these droplets are collected manually and analyzed in the lab with expensive instruments. Here, we introduce the Guttation Monitor, an on-site and low-cost monitoring technology for guttation droplets. It consists of three parts 1) a paper-based microfluidic chip that can collect guttation droplets and perform colorimetric detection of six chemicals, 2) a self-contained and solar-powered camera module that can capture the result from the chip, and 3) an end-user app that can interpret the result. We discuss this technology's design and implementation, conduct evaluations on tomato plants, conduct interviews, and envision how such a technology could enhance the human-plant relationship in four dimensions.

cs.HC↗

arXiv2022

Computational Co-Design for Variable Geometry Truss

Living creatures and machines interact with the world through their morphology and motions. Recent advances in creating bio-inspired morphing robots and machines have led to the study of variable geometry truss (VGT), structures that can approximate arbitrary geometries and has large degree of freedom to deform. However, they are limited to simple geometries and motions due to the excessively complex control system. While a recent work PneuMesh solves this challenge with a novel VGT design that introduces a selective channel connection strategy, it imposes new challenge in identifying effective channel groupings and control methods. Building on top of the hardware concept presented in PneuMesh, we frame the challenge into a co-design problem and introduce a learning-based model to find a sub-optimal design. Specifically, given an initial truss structure provided by a human designer, we first adopt a genetic algorithm (GA) to optimize the channel grouping, and then couple GA with reinforcement learning (RL) for the control. The model is tailored to the PneuMesh system with customized initialization, mutation and selection functions, as well as the customized translation-invariant state vector for reinforcement learning. The result shows that our method enables a robotic table-based VGT to achieve various motions with a limited number of control inputs. The table is trained to move, lower its body or tilt its tabletop to accommodate multiple use cases such as benefiting kids and painters to use it in different shape states, allowing inclusive and adaptive design through morphing trusses.

cs.AI↗

arXiv2021

FoamFactor: Hydrogel-Foam Composite with Tunable Stiffness and Compressibility

This paper presents FoamFactor, a novel material with tunable stiffness and compressibility between hydration states, and a tailored pipeline to design and fabricate artifacts consisting of it. This technique compounds hydrogel with open-cell foams via additive manufacturing to produce a water-responsive composite material. Enabled by the large volumetric changes of hydrogel dispersions, the material is soft and compressible when dehydrated and becomes stiffer and rather incompressible when hydrated. Leveraging this material property transition, we explore its design space in various aspects pertaining to the transition of hydration states, including multi-functional shoes, amphibious cars, mechanical transmission systems, and self-deploying robotic grippers.

cs.HC↗

arXiv2020

SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows

Morphing materials allow us to create new modalities of interaction and fabrication by leveraging dynamic behaviors of materials. Yet, despite the ongoing rapid growth of computational tools within this realm, current developments are bottlenecked by the lack of an effective simulation method. As a result, existing design tools must trade-off between speed and accuracy to support a real-time interactive design scenario. In response, we introduce SimuLearn, a data-driven method that combines finite element analysis and machine learning to create real-time (0.61 seconds) and truthful (97% accuracy) morphing material simulators. We use mesh-like 4D printed structures to contextualize this method and prototype design tools to exemplify the design workflows and spaces enabled by a fast and accurate simulation method. Situating this work among existing literature, we believe SimuLearn is a timely addition to the HCI CAD toolbox that can enable the proliferation of morphing materials.

cs.HC↗

arXiv2019

Material characterization and precise finite element analysis of fiber reinforced thermoplastic composites for 4D printing

Four-dimensional (4D) printing, a new technology emerged from additive manufacturing (3D printing), is widely known for its capability of programming post-fabrication shape-changing into artifacts. Fused deposition modeling (FDM)-based 4D printing, in particular, uses thermoplastics to produce artifacts and requires computational analysis to assist the design processes of complex geometries. However, these artifacts are weak against structural loads, and the design quality can be limited by less accurate material models and numerical simulations. To address these issues, this paper propounds a composite structure design made of two materials - polylactic acid (PLA) and carbon fiber reinforced PLA (CFPLA) - to increase the structural strength of 4D printed artifacts and a workflow composed of several physical experiments and series of dynamic mechanical analysis (DMA) to characterize materials. We apply this workflow to 3D printed samples fabricated with different printed parameters to accurately characterize the materials and implement a sequential finite element analysis (FEA) to achieve accurate simulations. The accuracy of deformation induced by the triggering process is both computationally and experimentally verified with several creative design examples, and the 95% confidence interval of the accuracy is (0.972, 0.985). We believe the presented workflow is essential to the combination of geometry, material mechanism and design, and has various potential applications.

cs.CG↗

arXiv2019

Robotic Materials

The Computing Community Consortium (CCC) sponsored a workshop on "Robotic Materials" in Washington, DC, that was held from April 23-24, 2018. This workshop was the second in a series of interdisciplinary workshops aimed at transforming our notion of materials to become "robotic", that is have the ability to sense and impact their environment. Results of the first workshop held from March 10-12, 2017, at the University of Colorado have been summarized in a visioning paper (Correll, 2017) and have identified the key technological challenges of "Robotic Materials", namely the ability to create smart functionality with a minimum of additional wiring by relying on wireless power and communication. The goal of this second workshop was to turn these findings into recommendations for government action. Computation will become an important part of future material systems and will allow materials to analyze, change, store and communicate state in ways that are not possible using mechanical or chemical processes alone. What "computation" is and what is possibilities are, is unclear to most material scientists, while computer scientists are largely unaware of recent advances in so-called active and smart materials. This gap is currently shrinking, with computer scientists embracing neural networks and material scientists actively researching novel substrates such as memristors and other neuromorphic computing devices. Further pursuing these ideas will require an emphasis on interdisciplinary collaboration between chemists, engineers, and computer scientists, possibly elevating humankind to a new material age that is similarly disruptive as the leap from the stone to the plastic age.

cs.RO↗