Constantinos Chamzas

prof_pic.jpg

UH271, Unity Hall

100 Institute Rd

Worcester, MA 01609

I am an Assistant Professor in Robotics Engineering at Worcester Polytechnic Institute.

At WPI, I lead the Efficient Learning for Planning and Intelligent Systems (ELPIS) Lab. The lab focuses on autonomous robotic systems capable of reasoning about and interacting with the physical world. The primary goal is to develop agents that are efficient, robust, and capable of learning from real-world interactions. The main research areas of the lab are planning efficiency, planning under uncertainty, and planning from visual inputs. For more information, see this page.

I have received my Diploma from Aristotle University of Thessaloniki as an Electrical and Computer Engineer in 2017. I received my Ph.D. in Computer Science in 2023 at Rice University, working under the supervision of Dr. Lydia Kavraki and Dr. Anshumali Shrivastava. I am honored to have received an NSF-GRFP fellowship for my doctoral studies. More details can be found in my curriculum vitae.

If you are a prospective student please see this page.

news

May 30, 2024 The ELPIS lab webpage is now live! Check it out here.
Apr 20, 2024 I was invited as speaker at the ECESCON15 conference on and gave talk about “Learning for Robot Motion Planning”.
Oct 03, 2023 I was an invited speaker at the Methods for Objective Comparison of Results in Intelligent Robotics Research workshop in IROS2023.
Jul 01, 2023 I officially joined the Robotics Engineering Department of Worcester Polytechnic Institute as an Assistant Professor!
Sep 17, 2022 I was named a Future Faculty Fellow by the School of Engineering at Rice University.

selected publications

  1. AR
    Sampling-Based Motion Planning: A Comparative Review
    A. Orthey, C. Chamzas, and L. Kavraki
    Annual Review of Control, Robotics, and Autonomous Systems, 2024
  2. Learning to Retrieve Relevant Experiences for Motion Planning
    C. Chamzas, A. Cullen, A. Shrivastava, and L. E. Kavraki
    In IEEE International Conference on Robotics and Automation, 2022
  3. RAL
    MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
    C. Chamzas, C. Quintero-Peña, Z. Kingston, A. Orthey, D. Rakita, M. Gleicher, M. Toussaint, and L. E. Kavraki
    IEEE Robotics and Automation Letters, 2022
  4. Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
    C. Chamzas, Z. Kingston, C. Quintero-Peña, A. Shrivastava, and L. E. Kavraki
    In IEEE International Conference on Robotics and Automation, 2021
    (Top-4 finalist for best paper in Cognitive Robotics)