Max Robotics — Global Service Robot Directory + US Certification

About

Hamed Ayoobi’s research lies at the intersection of robotics, machine learning, and argumentation theory, with a focus on enabling autonomous systems to adapt and reason in dynamic, unpredictable environments. His major contributions include developing the **Local Hierarchical Dirichlet Process (Local-HDP)**, a non-parametric Bayesian method for open-ended 3D object category recognition that allows robots to incrementally learn and adapt to new objects in real-time—a critical capability for general-purpose service robots. Ayoobi also pioneered **argumentation-based online incremental learning**, which equips robots to autonomously handle unforeseen failures by reasoning about and learning from novel situations without human intervention. His work on **occluded 3D object segmentation** further extends these methods to cluttered, real-world scenarios. With over 35 citations across his key papers, Ayoobi’s research has been recognized for its practical impact in robotics, particularly in enabling lifelong learning and robust failure recovery. Notably, his 2021 paper on argumentation-based learning and the Local-HDP method have been cited in top venues, highlighting their influence on the fields of autonomous robotics and open-ended learning.