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About

Shih-Yun Lo’s research lies at the intersection of human-robot interaction, motion planning, and multi-agent teamwork, with a focus on making mobile robots safe, efficient, and socially aware in human environments. Her most influential work examines how self-balancing mobile robots can navigate crowded spaces using pedestrian avoidance strategies that are not only physically safe but also perceived as comfortable by humans—a contribution that has garnered 22 citations. Lo is also the architect of the PETLON algorithm (Planning Efficiently for Task-Level-Optimal Navigation), which enables robots to sequence high-level goals and subgoals in large-scale indoor environments while reasoning about human locations and task constraints. This work, published in 2018 and 2020, has collectively earned 28 citations and represents a significant advance in integrated task and motion planning. Additionally, Lo has explored multi-agent planning under uncertainty, developing methods for information-revealing communication in teamwork and robust motion planning in human workspaces using Markov Decision Processes. Her research is notable for bridging theoretical planning algorithms with practical, human-aware robot behavior, making her a rising voice in socially intelligent robotics.