arXiv subjects

Alexander Htet Kyaw

Publications and source records attributed to Alexander Htet Kyaw.

13 recordsLinked to original sources

arXiv2026

Reconfigurable Structural Robotic Assembly: Interlocking 3D Aggregations with Self-Aligning Compound Nested Lattice Modules

Robotic construction systems often treat the material system and the robot as separate design problems, locating intelligence primarily in hardware, sensing, motion planning, and control. This project instead investigates how geometric intelligence can be encoded within architected material systems to simultaneously address requirements for robotic grasping, self-alignment, reversible connection, structural performance, and three-dimensional aggregation. We introduce a self-aligning compound nested lattice module composed of conjoined cuboctahedral-octahedral units. The cuboctahedral features of the modules provide defined surfaces for robotic grasping and alignment, while the octahedral features incorporate screw-releasable snap-fit connectors and corresponding receptors. Additionally, we present a nested arrangement that enables interlocking aggregation along the x, y, and z axes. We demonstrate the system through furniture and architectural scale structures assembled using both a robotic arm and mobile assembler. The resulting configurations include seating, spanning structures, surfaces, and vertical frames. Compression testing of the compound module produced a stiffness of 4,556 N/mm, a maximum load of 3,445 N, and a compressive modulus of 17.5 MPa. The modules can also be disassembled and reused across different configurations, supporting reconfigurable and circular construction.

cs.RO↗

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↗

arXiv2025

Text to Robotic Assembly of Multi Component Objects using 3D Generative AI and Vision Language Models

Advances in 3D generative AI have enabled the creation of physical objects from text prompts, but challenges remain in creating objects involving multiple component types. We present a pipeline that integrates 3D generative AI with vision-language models (VLMs) to enable the robotic assembly of multi-component objects from natural language. Our method leverages VLMs for zero-shot, multi-modal reasoning about geometry and functionality to decompose AI-generated meshes into multi-component 3D models using predefined structural and panel components. We demonstrate that a VLM is capable of determining which mesh regions need panel components in addition to structural components, based on the object's geometry and functionality. Evaluation across test objects shows that users preferred the VLM-generated assignments 90.6% of the time, compared to 59.4% for rule-based and 2.5% for random assignment. Lastly, the system allows users to refine component assignments through conversational feedback, enabling greater human control and agency in making physical objects with generative AI and robotics.

cs.RO↗

arXiv2025

Augmented Assembly: Object Recognition and Hand Tracking for Adaptive Assembly Instructions in Augmented Reality

Recent advances in augmented reality (AR) have enabled interactive systems that assist users in physical assembly tasks. In this paper, we present an AR-assisted assembly workflow that leverages object recognition and hand tracking to (1) identify custom components, (2) display step-by-step instructions, (3) detect assembly deviations, and (4) dynamically update the instructions based on users' hands-on interactions with physical parts. Using object recognition, the system detects and localizes components in real time to create a digital twin of the workspace. For each assembly step, it overlays bounding boxes in AR to indicate both the current position and the target placement of relevant components, while hand-tracking data verifies whether the user interacts with the correct part. Rather than enforcing a fixed sequence, the system highlights potential assembly errors and interprets user deviations as opportunities for iteration and creative exploration. A case study with LEGO blocks and custom 3D-printed components demonstrates how the system links digital instructions to physical assembly, eliminating the need for manual searching, sorting, or labeling of parts.

cs.HC↗

arXiv2025

AI Assisted AR Assembly: Object Recognition and Computer Vision for Augmented Reality Assisted Assembly

We present an AI-assisted Augmented Reality assembly workflow that uses deep learning-based object recognition to identify different assembly components and display step-by-step instructions. For each assembly step, the system displays a bounding box around the corresponding components in the physical space, and where the component should be placed. By connecting assembly instructions with the real-time location of relevant components, the system eliminates the need for manual searching, sorting, or labeling of different components before each assembly. To demonstrate the feasibility of using object recognition for AR-assisted assembly, we highlight a case study involving the assembly of LEGO sculptures.

cs.CV↗

arXiv2025

Scene-Aware Urban Design: A Human-AI Recommendation Framework Using Co-Occurrence Embeddings and Vision-Language Models

This paper introduces a human-in-the-loop computer vision framework that uses generative AI to propose micro-scale design interventions in public space and support more continuous, local participation. Using Grounding DINO and a curated subset of the ADE20K dataset as a proxy for the urban built environment, the system detects urban objects and builds co-occurrence embeddings that reveal common spatial configurations. From this analysis, the user receives five statistically likely complements to a chosen anchor object. A vision language model then reasons over the scene image and the selected pair to suggest a third object that completes a more complex urban tactic. The workflow keeps people in control of selection and refinement and aims to move beyond top-down master planning by grounding choices in everyday patterns and lived experience.

cs.CV↗

arXiv2025

Node-Based Editing for Multimodal Generation of Text, Audio, Image, and Video

We present a node-based storytelling system for multimodal content generation. The system represents stories as graphs of nodes that can be expanded, edited, and iteratively refined through direct user edits and natural-language prompts. Each node can integrate text, images, audio, and video, allowing creators to compose multimodal narratives. A task selection agent routes between specialized generative tasks that handle story generation, node structure reasoning, node diagram formatting, and context generation. The interface supports targeted editing of individual nodes, automatic branching for parallel storylines, and node-based iterative refinement. Our results demonstrate that node-based editing supports control over narrative structure and iterative generation of text, images, audio, and video. We report quantitative outcomes on automatic story outline generation and qualitative observations of editing workflows. Finally, we discuss current limitations such as scalability to longer narratives and consistency across multiple nodes, and outline future work toward human-in-the-loop and user-centered creative AI tools.

cs.HC↗

arXiv2025

Hierarchical Discrete Lattice Assembly: An Approach for the Digital Fabrication of Scalable Macroscale Structures

Although digital fabrication processes at the desktop scale have become proficient and prolific, systems aimed at producing larger-scale structures are still typically complex, expensive, and unreliable. In this work, we present an approach for the fabrication of scalable macroscale structures using simple robots and interlocking lattice building blocks. A target structure is first voxelized so that it can be populated with an architected lattice. These voxels are then grouped into larger interconnected blocks, which are produced using standard digital fabrication processes, leveraging their capability to produce highly complex geometries at a small scale. These blocks, on the size scale of tens of centimeters, are then fed to mobile relative robots that are able to traverse over the structure and place new blocks to form structures on the meter scale. To facilitate the assembly of large structures, we introduce a live digital twin simulation tool for controlling and coordinating assembly robots that enables both global planning for a target structure and live user design, interaction, or intervention. To improve assembly throughput, we introduce a new modular assembly robot, designed for hierarchical voxel handling. We validate this system by demonstrating the voxelization, hierarchical blocking, path planning, and robotic fabrication of a set of meter-scale objects.

cs.RO↗

arXiv2025

Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly

We present a system that transforms speech into physical objects using 3D generative AI and discrete robotic assembly. By leveraging natural language, the system makes design and manufacturing more accessible to people without expertise in 3D modeling or robotic programming. While generative AI models can produce a wide range of 3D meshes, AI-generated meshes are not directly suitable for robotic assembly or account for fabrication constraints. To address this, we contribute a workflow that integrates natural language, 3D generative AI, geometric processing, and discrete robotic assembly. The system discretizes the AI-generated geometry and modifies it to meet fabrication constraints such as component count, overhangs, and connectivity to ensure feasible physical assembly. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a robotic arm.

cs.RO↗

arXiv2025

Making Physical Objects with Generative AI and Robotic Assembly: Considering Fabrication Constraints, Sustainability, Time, Functionality, and Accessibility

3D generative AI enables rapid and accessible creation of 3D models from text or image inputs. However, translating these outputs into physical objects remains a challenge due to the constraints in the physical world. Recent studies have focused on improving the capabilities of 3D generative AI to produce fabricable outputs, with 3D printing as the main fabrication method. However, this workshop paper calls for a broader perspective by considering how fabrication methods align with the capabilities of 3D generative AI. As a case study, we present a novel system using discrete robotic assembly and 3D generative AI to make physical objects. Through this work, we identified five key aspects to consider in a physical making process based on the capabilities of 3D generative AI. 1) Fabrication Constraints: Current text-to-3D models can generate a wide range of 3D designs, requiring fabrication methods that can adapt to the variability of generative AI outputs. 2) Time: While generative AI can generate 3D models in seconds, fabricating physical objects can take hours or even days. Faster production could enable a closer iterative design loop between humans and AI in the making process. 3) Sustainability: Although text-to-3D models can generate thousands of models in the digital world, extending this capability to the real world would be resource-intensive, unsustainable and irresponsible. 4) Functionality: Unlike digital outputs from 3D generative AI models, the fabrication method plays a crucial role in the usability of physical objects. 5) Accessibility: While generative AI simplifies 3D model creation, the need for fabrication equipment can limit participation, making AI-assisted creation less inclusive. These five key aspects provide a framework for assessing how well a physical making process aligns with the capabilities of 3D generative AI and values in the world.

cs.RO↗

arXiv2025

AR Glulam: Accurate Augmented Reality Using Multiple Fiducial Markers for Glulam Fabrication

Recent advancements in Augmented Reality (AR) have demonstrated applications in architecture, design, and fabrication. Compared to conventional 2D construction drawings, AR can be used to superimpose contextual instructions, display 3D spatial information and enable on-site engagement. Despite the potential of AR, the widespread adoption of the technology in the industry is limited by its precision. Precision is important for projects requiring strict construction tolerances, design fidelity, and fabrication feedback. For example, the manufacturing of glulam beams requires tolerances of less than 2mm. The goal of this project is to explore the industrial application of using multiple fiducial markers for high-precision AR fabrication. While the method has been validated in lab settings with a precision of 0.97, this paper focuses on fabricating glulam beams in a factory setting with an industry manufacturer, Unalam Factory.

cs.ET↗

arXiv2025

Gesture Recognition for Feedback Based Mixed Reality and Robotic Fabrication: A Case Study of the UnLog Tower

Mixed Reality (MR) platforms enable users to interact with three-dimensional holographic instructions during the assembly and fabrication of highly custom and parametric architectural constructions without the necessity of two-dimensional drawings. Previous MR fabrication projects have primarily relied on digital menus and custom buttons as the interface for user interaction with the MR environment. Despite this approach being widely adopted, it is limited in its ability to allow for direct human interaction with physical objects to modify fabrication instructions within the MR environment. This research integrates user interactions with physical objects through real-time gesture recognition as input to modify, update or generate new digital information enabling reciprocal stimuli between the physical and the virtual environment. Consequently, the digital environment is generative of the user's provided interaction with physical objects to allow seamless feedback in the fabrication process. This research investigates gesture recognition for feedback-based MR workflows for robotic fabrication, human assembly, and quality control in the construction of the UnLog Tower.

cs.HC↗

arXiv2025

Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output

Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After the model generates an interior design image, a post-processing module identifies the top ten materials present and pairs them with carbon dioxide equivalent (CO2e) values from a general materials dictionary. This approach allows designers to immediately evaluate environmental impacts and refine prompts accordingly. We evaluate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along with quantitative CO2e data included in the generative AI outputs. Our qualitative and quantitative analyses reveal that the introduction of sustainability metrics in the third test leads to more informed design decisions, however, it can also trigger decision fatigue and lower overall satisfaction. Nevertheless, the majority of participants reported incorporating sustainability principles into their workflows in the third test, underscoring the potential of integrated metrics to guide more ecologically responsible practices. Our findings showcase the importance of balancing design freedom with practical constraints, offering a clear path toward holistic, data-driven solutions in AI-assisted architectural design.

cs.HC↗