Theodor Chakhachiro
University of Michigan–Ann Arbor, American University of Beirut
Papers
4
Total Citations
26
H-Index
3
About
Theodor Chakhachiro is a robotics researcher whose work spans state estimation, human-robot interaction, and socially-aware navigation. His most impactful contribution is a fully proprioceptive slip-velocity-aware state estimator for mobile robots, which uses invariant Kalman filtering and a disturbance observer to achieve robust localization without external sensors—a paper that has garnered 17 citations since 2023. He also developed the Providers-Clients-Robots (PCR) framework, a novel spatial-semantic planning approach for shared understanding in socially assistive robotics, enabling intuitive human-robot collaboration. In dynamic environments, Chakhachiro proposed SoLo T-DIRL, a socially-aware dynamic local planner based on trajectory-ranked deep inverse reinforcement learning that explicitly models social interaction factors for safer navigation in crowded spaces. Additionally, he introduced a method for map alignment assessment using synthetic displacement fields, contributing to autonomous mapping quality evaluation. His work demonstrates a strong focus on making robots more perceptive, socially intelligent, and reliable in real-world settings—key achievements for advancing mobile robotics and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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