Project Description

In modern industrial manufacturing, integrating multi-robot and multi-human systems that collaborate presents both significant opportunities and challenges, primarily concerning human safety, task efficiency, and effective coordination. Traditional robotic systems, often encumbered by physical barriers for safety, are costly and inflexible, limiting their applicability in dynamic manufacturing environments. Recent research highlights advancements in human-robot collaboration (HRC) through technologies like collaborative robots and artificial intelligence, which enhance interaction and coordination. Despite these advancements, current HRC systems often fail to seamlessly integrate human safety with task efficiency, and both multi-robot and multi-human coordination remain suboptimal, particularly in complex assembly processes. To address these gaps, we propose developing intelligent multi-robot systems that dynamically collaborate with multiple humans in manufacturing assembly tasks. This involves implementing context-aware safety protocols that use real-time data to prevent hazards without compromising efficiency, and developing algorithms for real-time task allocation that adapt to the changing capabilities of both human workers and robots. Additionally, AI-driven coordination mechanisms will enhance the symbiosis between multi-robot systems and human operators, ensuring smoother and more efficient collaboration. Our research aims to create a safer, more efficient manufacturing environment that leverages the strengths of both humans and robots, ultimately improving productivity and flexibility in industrial manufacturing.

Start Date

Spring 2026

Postdoc Qualifications

With dual Ph.D. degrees in Computer Science and Engineering (University of Bologna and University of Sussex in UK) and my current appointment as Postdoctoral Research Fellow and Assistant Research Professor at Ontario Tech University, I have developed a multidisciplinary expertise at the intersection of artificial intelligence, robotics, control, and human-centered systems. The Gilbreth Fellowship’s focus on interdisciplinary innovation and academic career development strongly aligns with my goal of becoming a leading faculty researcher advancing safe, efficient, and responsible multi-agent autonomy in manufacturing and industrial systems. My proposed project, “FLEX: Facilitating Lean and Efficient X-operations in Multi-Robot and Multi-Human Collaborative Manufacturing,” under the guidance of Prof. Yu She (Industrial Engineering) and Prof. Shaoshuai Mou (Aeronautics and Astronautics), will explore new AI-driven frameworks for context-aware safety, adaptive task allocation, and distributed optimization in multi-robot–human collaboration. Building upon my prior research in autonomous systems, explainable AI, and resilient robotic coordination, I aim to develop novel algorithms and cognitive control architectures that enable robots to dynamically reason about human intent, task context, and operational constraints in real time.

Co-advisors

Prof. Yu She (Industrial Engineering) and Prof. Shaoshuai Mou (Aeronautics and Astronautics) 

Bibliography

[1] Yunhai Han, Rahul Batra, Nathan Boyd, Tuo Zhao, Yu She, Seth Hutchinson, Ye Zhao, “Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer”, IEEE/ASME Transactions on Mechatronics (TMECH), 2024, DOI: 10.1109/TMECH.2024.3400789.
[2] Neha Sunil, Shaoxiong Wang, Yu She, Edward Adelson, and Alberto Rodriguez Garcia. "Visuotactile affordances for cloth manipulation with local control." In Conference on Robot Learning, pp. 1596-1606. PMLR, 2023.
[3] Yu She, Shaoxiong Wang, Siyuan Dong, Neha Sunil, Alberto Rodriguez and Edward Adelson, “Cable Manipulation with a Tactile-Reactive Gripper.” International Journal of Robotics Research, August 2021. doi:10.1177/02783649211027233.
[4] Rai, Ayush, Shaoshuai Mou, and Brian DO Anderson. "A Distributed Algorithm for Solving Linear Equations in Clustered Multi-Agent Systems." IEEE Control Systems Letters (2023).
[5] Z. Lu, T. Zhou and S. Mou. Real-Time Multi-Robot Planning in Cluttered Environment. Robotics 13(3), 2024.