Andrei Stoian

Papers

Total Citations

534

H-Index

About

Andrei Stoian is a leading voice at the intersection of machine learning and robotics, with a focused expertise in continual learning (CL). His seminal work, "Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges" (2019), has garnered over 490 citations, establishing him as a key architect of the field. Stoian’s major contribution lies in formalizing the paradigm of continual learning for robotic systems, where data distributions and learning objectives shift over time. He provides a comprehensive framework that models learning as a sequence of experiences, addressing the critical challenge of how robots can adapt and accumulate knowledge without catastrophic forgetting. This foundational work not only defines the problem space but also surveys strategies and opportunities, making it an essential resource for researchers. By bridging theoretical CL concepts with practical robotic applications, Stoian has paved the way for more adaptive, lifelong-learning autonomous systems. His research is indispensable for anyone exploring how machines can learn continuously in dynamic, real-world environments.