About

Robert Leicht is a Professor in the Department of Architectural Engineering at Penn State University. His research areas include building construction, education, and the integration of innovative technologies in the construction industry. His work encompasses a broad range of topics such as building energy solutions, high-performance structural systems, modeling, simulation, diagnostics under uncertainty, automation, robotics, and digital twins in construction. Leicht has contributed to advancing understanding in these fields through extensive research and publication efforts. His scholarly work involves developing interactive workspaces, exploring the barriers and opportunities for women in construction, and evaluating the sustainability of construction projects. He has also focused on the application of virtual reality, energy retrofit projects, and collaborative information management processes in construction education and practice. Leicht's research emphasizes interdisciplinary learning, the use of emerging technologies, and improving team performance and communication within the architecture, engineering, and construction industries.

Research topics

• Computer Science
• Engineering
• Artificial Intelligence
• Psychology
• Multimedia
• Economics
• Risk analysis (engineering)
• Engineering management
• Construction engineering
• Business
• Software engineering
• Economic growth
• Process management
• Mathematics education

Selected publications

• A planning schema of on-site construction robot operation

  Journal of Information Technology in Construction · 2026-02-16

  articleOpen accessSenior author

  The technology development for construction is leading to a variety of construction robots that have been, and continue to be, developed and implemented. When planning robot operation on a construction site, a wide variety of information is needed to support their safe and effective deployment. However, few researchers have summarized and structured the required information for planning construction robots' on-site operation. Therefore, this study developed a schema for planning and operating construction robots. The schema contains the planning information needed for construction robots to operate on-site, and the information is structured to enable planners to collect and query the needed information when assessing robot implementation. This study used a systematic literature review to identify the information needed for robots’ construction work. The review focused on the information that users need in the case of operating the robot on site, and the description of that information. To validate the schema, the study interviewed industry experts experienced in deploying construction robots and testing the schema through a database to verify the scope of housed information and efficiency of information acquisition. The developed and validated construction robot schema has four categories, including physical properties, operational requirements, safety, and activity. There are 56 attributes housed in the schema with definitions, examples, and data types. The information in the schema can help construction teams and planners comprehend the configuration and function of the robot, which can facilitate planning operations or deploying robots to specific tasks. Future work will further improve the planning process by observing and recording the on-site operations of construction robots.

  PublisherOA PDFDOI

• Developing a Taxonomy of Spatial Information for Augmented Reality Training: Design Strategies to Enhance Procedural Activity Performance

  2026-01-28

  articleSenior author

  Augmented reality (AR) training has the potential to enhance learning by overlaying virtual content onto physical spaces and representing abstract concepts through multisensory cues. However, AR designers face challenges in determining the optimal amount and types of information to enhance learning without causing cognitive overload. This paper proposes a spatial information taxonomy developed through activity component analysis, which decomposes tasks into fundamental behavioral processes and provides a systematic approach to identifying essential information types and their representations in AR training applications. By analyzing prior AR training scenarios using video coding methods, the authors identified key information types—such as instruction, interaction guidance, and feedback—and corresponding representation forms, including Panel, Glyph, Ghost, Trajectory, and others. The taxonomy offers a structured framework to guide designers in integrating pedagogically effective spatial information. Future work will validate the taxonomy and examine conditions under which specific information types and representations best optimize learning outcomes.

  PublisherDOI

• Comparative Analysis for Robot-Assisted Construction Processes: Task Definition and Model Development

  2026-01-28

  articleSenior author

  As the potential for robots in design and construction continues to grow, transitioning from traditional, process-based construction planning to robot-assisted construction necessitates a more precise definition of construction tasks. This paper examines existing process models for mobile robotic platforms equipped with robotic arms, focusing on identifying shared elements and key operational characteristics. The research emphasizes the development of process models and the identification of key requirements to facilitate analysis and standardization efforts. Initial process models were formulated based on commercially available construction robots, incorporating inputs, processes, and outputs from the literature and site visits to clarify both robotic construction processes and operator tasks. The analysis highlights generalized processes for key tasks such as mobilization, task implementation, and demobilization, as well as the ability of robots to use digital information for task execution. Results show that shared workflow elements, such as navigation and calibration, can serve as a foundation for standardization, while task-specific differences highlight the need for adaptability in robotic operations. The proposed model serves as a tool for swiftly assessing the specific requirements of new robot operations and construction planning, distinguishing platform vs. manipulator functions, understanding operator needs, and considering levels of autonomy while also providing an effective evaluation for future robot applications. Moreover, the paper explores methods for analyzing core tasks, shared attributes between operators and relevant personnel (e.g., crafts workers), and common information needs. Finally, strategies for validating and comparing these process models are proposed to ensure their accuracy and to deepen understanding of requirements and attributes through industry examples.

  PublisherDOI

• Capabilities of On-Site Construction Robots from the Sense–Plan–Act Framework

  2026-01-28

  articleSenior author

  The development and application of construction robots are helping the construction industry address multiple challenges, from safety to a diminishing pool of skilled labor. However, operating robots on construction sites is challenging, and the complex and dynamic construction environment requires robots to have capabilities to sense, plan, and act, which differ from operations in controlled environments. This study uses a systematic literature review based on the sense–plan–act (SPA) framework to extract information about mobile construction robot operations. The information was then categorized to obtain the robots’ capabilities for construction projects. The study summarizes the relationship among these capabilities and extends the SPA framework using an IDEF0 process model. The results focus on guiding construction industry users and robotics companies to understand the capabilities and relationships of construction robots when performing on-site construction operations. These capabilities and relationships can be used to select appropriate robot solutions based on construction task requirements.

  PublisherDOI

• Assessment instrument for lean journeys in integrated project delivery: lean learning health assessment (LLHA)

  Engineering Construction & Architectural Management · 2026-01-22

  articleOpen accessSenior author

  Purpose The Architecture, Engineering, and Construction (AEC) industry is witnessing a growth in the implementation of Lean principles, in particular, by teams adopting Integrated Project Delivery (IPD). This growth requires participants in these teams to possess knowledge of Lean and IPD. However, the practitioners might not have the time to participate in continuous training, nor the metacognitive awareness of their knowledge gap in these areas. This study develops an instrument to support participants in IPD teams in assessing their knowledge in implementing Lean in their projects. The instrument also aims to support these participants in gaining a metacognitive awareness of their knowledge gap and self-regulate their Lean learning journey. Design/methodology/approach The instrument was designed by leveraging literature on IPD, Lean Construction and self-regulated learning in educational psychology. Five semi-structured interviews were conducted to evaluate the instrument's face and content validity. The instrument was then deployed on three IPD projects, and survey data collected from these projects were used to validate its effectiveness. A principal component analysis was conducted to identify the most influential factors in self-regulated lean Learning assessment. Findings Based on the data from the three projects, owners and trade contractors in IPD projects implementing Lean tend to place more emphasis on Lean topics focused on understanding and fostering a collaborative environment. On the other hand, Architects, design engineers and other participants emphasize on learning and working towards a project environment based on trust. Research limitations/implications These findings inform participants on their current strengths in Lean implementation, which can be capitalized on for better project outcomes. Further, it helps identify areas that participants need to place more emphasis on their Lean journey to achieve improvements. Practical implications Through this instrument, the authors aim to support a better understanding of gaps in learning and implementing Lean principles by IPD project participants. Originality/value This paper develops a unique instrument that demonstrates how self-directed learning can be leveraged to assess the lean learning journeys of construction industry practitioners.

  PublisherOA PDFDOI

• Exploring Classification Features of On-Site Construction Robots

  2025-12-11

  articleSenior authorCorresponding

  Robotics is emerging as both a critical need and an opportunity in the construction industry. In recent decades, researchers have been working to make robots more involved in construction production, with recent emphasis on moving from use in prefabrication to field construction. As robotics and automation technology are rapidly evolving, more robots are being developed and implemented to contribute to construction. In addition to this, as the construction industry explores the need for automation, a variety of different construction activities are expected to be completed or improved through the implementation of robotics. However, when construction planners decide to implement robots in a project, selecting the proper robot based on the needs of the specific task becomes a new challenge. This study explores options for how to aggregate and classify features for construction robots and organize these features to help construction planners. To address this challenge, this study explored robot classification characteristics applied in various industries and organized these characteristics according to the implementation needs of robots in the construction industry. This study considered classification features in two domains, including industrial robots and construction-specific robots. Through literature review, this study collected the classification features, including application, locomotion, level of autonomy, use case, and basic task. Based on each classification feature, this study further collected and summarized the corresponding classification elements.

  PublisherDOI

• Evaluation of Parameters that Impact Data Collection for Robot Progress Detection of a Masonry Wall

  Journal of Computing in Civil Engineering · 2025-11-25

  article

  The construction industry is going through a technological revolution where aspects of digitalization are combined with automation. A prime example is using data from a building information model (BIM) to promote robotic construction. However, numerous parameters impact the robot’s ability to collect data and provide user feedback for site progress detection. For instance, during progress detection, the robot must recognize site work features, such as a masonry wall. Therefore, this study investigates four parameters that would impact the data collection process for progress detection, including wall configuration, robot navigation path, image spacing, and distance from the wall. Photogrammetry is used to capture images with a teleoperated Husky A200 robot equipped with a stereo camera and the Global Positioning System (GPS). COLMAP was used to generate both the sparse and dense point cloud reconstruction and statistics related to the model, such as the reconstruction time, mean reprojection error, percentage of images used for reconstruction, and points generated. Once COLMAP processed all reconstructions, each model was reviewed individually. The results were filtered to determine viable methods for the data collection process and provide insight into which methods required further analysis to ascertain the quality of the model. However, the main goal of this study is to document and investigate the methods used in the data collection process while determining which methods should facilitate the optimal data collection process.

  PublisherDOI

• Developing Process Models for Semi-Autonomous Construction Robot Assessment

  2025-12-11

  articleSenior authorCorresponding

  Advances in robotics represent a potential shift in design and construction. Since construction planning is based on craft work, the transition to robotic-based construction requires tasks to be defined in much greater detail. In this paper, we begin to develop a standard rule-based process model of robot-influenced operation for mobile robotic platforms with attached robotic arms for performing field construction tasks. The paper focuses on the process model framework and requirements development to support future analysis and standardization efforts. The initial process is drafted for commercially available case study robots. The results present robot-related input, process, and output information from the literature to specify robotic construction processes and operator tasks. An initial analysis of the robotic process model was conducted to consider a generic robot process for specific tasks involving robotic mobilization, implementation, and demobilization. The paper highlights the necessity for the robot to possess independent capability and use BIM information to complete the required construction tasks successfully, thus laying the information foundation for the creation of a generic robotic process model. The process model contributes through the ability to quickly assess new robotic operations and unique requirements for construction planning or means and methods assessment of future robotic uses.

  PublisherDOI

• Beyond trial and error: toward construction-aware early design optimization considering robotic capabilities

  Architecture Structures and Construction · 2025-01-22 · 2 citations

  article
  PublisherDOI

• KHS&S – a Case Study of Lean Training for Trade Contractors

  Annual Conference of the International Group for Lean Construction · 2025-06-02

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

Frequent coauthors

• John Messner

  Pennsylvania State University

  148 shared

• Chimay Anumba

  95 shared

• Ralph Kreider

  94 shared

• Craig Dubler

  Pennsylvania State University

  88 shared

• Chitwan Saluja

  88 shared

• Sean Goodman

  87 shared

• Colleen Kasprzak

  87 shared

• Nevena Zikic

  87 shared

Labs

• Architectural Engineering LabPI

Education

• PhD, Architectural Engineering

  Pennsylvania State University

  2009

Awards & honors

• Outstanding Engineering Alumni Award
• ASAE Early Career Impact Award
• Penn State Engineering Alumni Society Awards
• Penn State Alumni Association Awards