Research Projects

FMRG: Cyber: Custom Product Manufacture at Metal Recycling Plants Using Artificial Intelligence, Extended Reality, and Human-Robot Collaboration - Funded by the National Science Foundation (NSF): 2025 - 2029 (Estimated)

My Role: Principal Investigator (September 2025 - Present)

Objective: This Future Manufacturing Research Grant (FMRG) project funds research that attempts to make novel strides in Future Manufacturing by contributing new cyber technologies to enable recyclofacturing, or custom manufacturing of products to user specifications using scrap or consumer material at metal recycling facilities. The project looks to contribute advances in artificial intelligence, extended reality, and human-robot collaboration to transform metal recycling facilities, that focus on traditionally low-skilled tasks, into new and agile product manufacturers, helping to keep manufacturing in the US. The project’s contributions could transform the expertise-heavy workflow of traditional computer-aided design and manufacturing into an intuitive workflow that enables recycling plant workers to translate an end user's arbitrary product specification into a product build, upon rapid upskilling to the novel cyber technologies. The education and workforce development plan seeks to improve the job outlook for current and future recycling plant workers by upskilling them to become new-collar workers through courses in prompt engineering for recyclofactured product design, extended reality-based weld training, and working with collaborative robots for assembly of recyclofactured products. The project seeks to transform recycling facilities into agile manufacturers attractive to future workers who increasingly look toward technology-enhanced trades. The team is partnering with 2- and 4-year colleges providing vocational training and workforce development, business development agencies, and groups supporting individuals in manufacturing and welding to conduct interviews, perform course development, and conduct evaluations. The project provides benefits to society by strengthening community/recycler networks to spur local product fabrication and enhancing self-sufficiency of local communities. 

The research looks to make fundamental contributions in three areas. First, it seeks to advance artificial intelligence by providing neural networks for automated generalized computer-aided design model generation from user product specifications. These models are anticipated to include text, images, and sketches, automated scrap metal sheet assessments, optimized model decomposition and facet assignment to sheets, and next step build predictions. Second, it looks to provide novel extended reality work for guidance to the human worker in the build process through overlay of expert recommendations and next step structure, enabling enhanced human-in-the-loop involvement. Third, it looks to develop human-robot collaboration for assembly of recyclofactured products with dynamic task allocation between humans and collaborative robots by integrating awareness of worker load, preferences, activity, and ergonomic risk. The Future of Work assessment integrates qualitative interviews with workers and managers, large-scale societal survey of expectations from cyber-enabled manufacturing, quantitative assessment of demand and production costs through willingness-to-pay experiments and labor cost estimation by connecting with research plan studies on technology interaction, and projection models to inform on long-term impact of recyclofacturing.

Related Publications: [1][2]

NSF-SNSF: VR-HRC: Virtual Reality-based Multi-Human-Multi-Robot Collaboration in Industrial Environments - Funded by the National Science Foundation (NSF): 2025 - 2028 (Estimated)

Objective: This joint National Science Foundation - Swiss National Science Foundation (NSF-SNSF) project aims to advance research in multi-human-multi-cobot collaboration in industrial settings. Collaborative Robots (Cobots) are increasingly being deployed in factories to assist workers with repetitive tasks or heavy lifting. Typically equipped with a Graphical User Interface and simulation software, these cobots are designed to be easily programmed by non-experts. However, while programming a single robot may be straightforward, coordinating multiple cobots to collaborate effectively with human workers can present challenges in a factory environment. During assembly processes, these robots often work alongside humans, helping to lift heavy components or provide necessary tools and materials as required. To effectively teach cobots their tasks, human operators must have a solid understanding of three-dimensional spatial processes. Simultaneously, the cobots must learn to interpret and adapt to human actions within their workspace. To address these challenges, this project introduces a Virtual Reality (VR) framework that facilitates collaboration between humans and cobots. The system aims to empower multiple users to interact using hand gestures, eye gaze, and speech within a physics-based VR simulation environment, thereby simplifying the process of teaching robots industrial tasks. The collaboration between Santa Clara University in the USA and the Dalle Molle Institute for Artificial Intelligence of the Scuola Universitaria Professionale della Svizzera Italiana in Switzerland will foster knowledge transfer and provide student researchers with cultural understanding, ultimately strengthening international research collaborations. This project will develop intent detection methods that leverage multimodal human demonstrations to generate actions for multiple collaborative robots (cobots) automatically. It involves creating an innovative physics-based virtual reality (VR) environment tailored for multi-cobot factory settings and employing generative AI techniques to model, generalize, and streamline the embodied reasoning between humans and multi-cobots. Additionally, the method incorporates human-in-the-loop approaches to refine the robot's behavior. The project encompasses three key thrusts: 1. Developing a VR interface for multi-human-multi-cobot collaboration, which will facilitate the collection of a multimodal multiperson dataset. 2. Establishing an intelligent framework that advances research in embodied reasoning and human-in-the-loop methodologies for the refinement of multi-cobot behavior. 3. Evaluating the proposed methods and systems through user studies, including a small pilot study with physical robots in real-world settings.