Portrait de Ali Ayub

Ali Ayub

Membre affilié
Professeur adjoint, Concordia University, Département d'informatique et de génie logiciel
Sujets de recherche
Apprentissage actif
Apprentissage continu
Apprentissage en ligne
IA appliquée
Interaction humain-robot
Robotique
Vision par ordinateur
Site web
Google Scholar
LinkedIn

Publications

Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning
Alvaro Gonzalez
M. H. Hasan Shovo
Ali Ayub
Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noi… (voir plus)sy conditions. Existing approaches often rely on complex spatio-temporal models, which can be computationally expensive and sensitive to environmental variability. In this paper, we propose GLOBE, a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning. The framework performs sequential prediction efficiently while selectively invoking LLM reasoning only when the model confidence is low. To evaluate performance under realistic conditions, we introduce HOMER-Noise, a noisy extension of the HOMER+ dataset that simulates structured disturbances such as object movements caused by humans, pets, and toddlers. Experimental results show that GLOBE achieves competitive performance with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings. The framework is further validated through a proof-of-concept integration with a Stretch 3 mobile manipulator, demonstrating its potential application in real-world human-robot interaction scenarios.
2026-06-06
arXiv (prépublication)
doi.org
arxiv.org
Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Evolving Assistance for Everyday Life
Bahar Irfan
Nikhil Churamani
Michelle Zhao
Rajat Kumar Jenamani
Ali Ayub
Silvia Rossi
Today's high-capacity generalist robot policies provide a strong foundation for broad task-level competence, yet achieving effective and equ… (voir plus)itable support for people in everyday settings remains a significant challenge. Real-world environments are dynamic and unstructured, and human needs evolve over time, requiring robots that can adapt accordingly. The ultimate evaluator of any robotic system is the person it assists, and personalization is essential to ensuring equitable and meaningful support across diverse users and contexts. Developing robots that can continually learn from interaction, adapt their behaviors over time, and flexibly assume roles as learners and collaborators is a critical step toward realizing effective integration of robots into daily life. With this year's theme of "Evolving Assistance for Everyday Life", and in alignment with the conference theme "HRI Empowering Society", the sixth edition of the "Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)" workshop aims to bring together insights across diverse disciplines, focusing on how robots can progressively adapt their support to suit diverse individuals, each with unique and changing needs, across real-world contexts. Through this lens, the workshop aims to discuss current and future directions in how assistive systems can flexibly respond, continually improve over time, and deliver more inclusive and empowering support in everyday life.
2026-03-15
International Conference on Human-Robot Interaction (publié)
doi.org