Dexter R. R. Scobee

Princeton University, University of California, Berkeley

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About

Dexter R. R. Scobee is a leading researcher at the intersection of multi-robot systems, human-robot collaboration, and safe autonomy. His work is defined by a dual focus: enabling scalable, provable coordination among robots and ensuring safety when those robots interact with people. Scobee’s most influential contribution is the Push-Swap-Wait (PSW) algorithm, a decentralized and complete approach for multi-robot motion planning in confined spaces. This work, his most cited with 18 citations, solved a fundamental scalability problem by adapting centralized “push and swap” paradigms for distributed execution. He has since advanced the field of human-cyber-physical systems, developing dynamic inverse models to characterize the coupling between robotic automation and human neuromechanical decision-making. A key achievement is his modeling of “supervisor safe sets,” a framework that optimizes how a human’s limited cognitive resources are allocated when overseeing robot teams, directly improving collaboration efficiency. Most recently, Scobee has pioneered approaches to safety in inverse reinforcement learning, addressing the critical challenge of ensuring autonomous systems can adapt safely to uncertain, human-centric environments. His work provides foundational theory for deploying trustworthy robots beyond industrial settings and into everyday life.