ΣΦΗΜΜΥ 15



Constantinos Chamzas


Constantinos Hamzas is an assistant professor in the Robotics Engineering Department at Worcester Polytechnic Institute in the United States of America. He obtained his PhD in 2023 from the Department of Computer Science at William Marsh Rice University, working under the supervision of Professors Lydias Kavrakis, and Anshumali Shrivastava. He obtained his degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2017. He is a recipient of the NSF-GRFP scholarship and a Future Faculty Fellow from Rice University. His research interests focus on the design of robotic motion algorithms, with an emphasis on combining classical algorithms with learning-based methods. His current research interests are improving the speed of motion design algorithms, motion with imprecise space models, and image-based motion design. His work on combining machine learning to improve the speed of design algorithms was nominated for the Best Paper Award in Cognitive Robotics at ICRA 2021.

Robotic Motion Design Algorithms and Machine Learning

Robotics has the potential to enhance human capabilities in ways that could transform human society. The integration of advanced sensors and modern motors has set the stage for robots to achieve these capabilities, as evidenced by their presence in factories and, in limited cases, domestic environments. However, in unfamiliar environments, robots struggle to adapt, cooperate with humans, and perform complex tasks. In this presentation, I will present recent developments towards bringing robots into the real world by integrating classical motion algorithms with modern machine learning methods. After an introduction to the basic concepts of motion design algorithms for robotic arms, this talk will present (a) recent findings on how learning methods can improve the speed of classical motion algorithms, (b) research on robotics movement with imprecise models of space and (c) research on creating algorithms for robotic movement directly from images.

Take me back to speakers!