Department of Aerospace Engineering

Abstract:
Driven by rapid advancements over the past decade, multi-robot systems have expanded beyond factory floors into unstructured environments, where they must coordinate seamlessly with both teammates and humans to accomplish diverse and complex tasks. As these systems take on increasingly sophisticated roles, ensuring their scalability, reliability, and performance presents significant theoretical and practical challenges. In this talk, I will delve into the intricacies of developing scalable multi-robot systems with performance guarantees, placing particular emphasis on the computational underpinnings of Multi-Robot Motion Planning (MRMP) with complex goals. In the first part of the talk, I will show how insights from Multi-Agent Path Finding (MAPF) can inform the design of scalable MRMP algorithms. Building on this foundation, in the second part, I will address deriving motion from high-level specifications. Finally, I will briefly highlight the critical role of explainability in deploying MRMP solutions in safety-critical domains and propose a novel approach to enhance the interpretability of generated plans.

Bio:
Morteza Lahijanian is an associate professor in the Aerospace Engineering Sciences department, an affiliated faculty at the Computer Science department and Robotics program, and the director of the Assured, Reliable, and Interactive Autonomous (ARIA) Systems group at the University of Colorado Boulder. He received a B.S. in Bioengineering at the University of California, Berkeley and a PhD in Mechanical Engineering at Boston University. He served as a postdoctoral scholar in Computer Science at Rice University. Prior to joining CU Boulder, he was a research scientist in the department of Computer Science at the University of Oxford. His awards include Outstanding Junior Faculty, Ella Mae Lawrence R. Quarles Physical Science Achievement Award, Jack White Engineering Physics Award, NSF GK-12 Fellowship, and Wadham College Research Fellowship.

Dr. Lahijanian's research interests span the areas of control theory, stochastic hybrid systems, formal methods, machine learning, and game theory with applications in robotics, particularly, motion planning, strategy synthesis, model checking, and human-robot interaction. His lab develops novel theoretical foundations and computational frameworks to enable reliable and intelligent autonomy. The emphasis is especially on safe autonomy through correct-by-construction algorithmic approaches.