Kostas Bekris

About

Professor Kostas Bekris leads the PRACSYS Lab at Rutgers University, focusing on research in robot learning, perception, and planning. His work emphasizes applications in manipulation and navigation for logistics, search and rescue, and service robotics. A particular area of interest is the development of novel soft mechanisms and robots that exhibit significant dynamics. The PRACSYS Lab, under his guidance, integrates physics-aware research for autonomous computational systems, reflecting a practical approach to robotics inspired by the concept of praxis from Ancient Greek philosophy. Professor Bekris is affiliated with the Computer Science Department, CBIM Research Center, RU Center for Cognitive Science, and CCICADA DHS Center of Excellence at Rutgers, contributing to interdisciplinary advancements in robotics and autonomous systems.

Research topics

• Computer Science
• Artificial Intelligence
• Human–computer interaction
• Computer vision
• Engineering
• Engineering drawing
• Psychology
• Materials science
• Mathematics
• Simulation

Selected publications

• Sim2Real in Robotics and Automation: Applications and Challenges

  IEEE Transactions on Automation Science and Engineering · 2021 · 148 citations

  To Perform reliably and consistently over sustained periods of time, large-scale automation critically relies on computer simulation. Simulation allows us and supervisory AI to effectively design, validate, and continuously improve complex processes, and helps practitioners to gain insight into the operation and justify future investments. While numerous successful applications of simulation in industry exist, such as circuit simulation, finite element methods, and computeraided design (CAD), st…

  DOI

• Complex In-Hand Manipulation Via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

  IEEE Robotics and Automation Letters · 2022 · 71 citations

  Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand’s kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different manipulation manifolds. This capability, however, has traditionally been difficult or impossible to practically realize. A finger gaiting system must simultaneously plan for and control forces on the o…

  DOI

• Vision-driven Compliant Manipulation for Reliable; High-Precision Assembly Tasks

  2021 · 58 citations

  Senior authorCorresponding

  Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception systems.This paper demonstrates that the combination of state-of-the-art object tracking with passively adaptive mechanical hardware can be leveraged to complete precision manipulation tasks with tight, industrially-relevant tolerances (0.25mm).The proposed control…

  DOI

• Modular shape-changing tensegrity-blocks enable self-assembling robotic structures

  Nature Communications · 2025-07-01 · 13 citations

  articleOpen access

  Modular robots are currently designed to perform a variety of tasks, primarily focusing on locomotion or manipulation through the reconfiguration of rigid modules. However, the potential to integrate multiple functions, such as making each robot deployable and capable of building lattice structures for self-construction and infrastructure creation, remains largely unexplored. To advance the field, we hypothesize that combining tensegrity principles with modular robotics can create lightweight, d…

  PublisherOA PDFDOI

• An Open-Source, Reproducible Tensegrity Robot That Can Navigate Among Obstacles

  IEEE Robotics and Automation Letters · 2026-04-06 · 1 citations

  articleSenior author

  Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering planning and obstacle avoidance. This paper presents a complete, open-source, and reproducible system that enables navigation for a 3-bar tensegrity robot. The system comprises: (i) an inexpensive, open-source hardware design, and (ii) an integrate…

  PublisherDOI

Recent grants

• Collaborative Research: RI: Medium: Robust Assembly of Compliant Modular Robots

  NSF · $386k · 2020–2026

• NRI: INT: COLLAB: Integrated Modeling and Learning for Robust Grasping and Dexterous Manipulation with Adaptive Hands

  NSF · $868k · 2017–2023

• BSF:2012166:A Framework for Composite Techniques in Motion Planning

  NSF · $40k · 2013–2018

Frequent coauthors

• Abdeslam Boularias

  42 shared

• Rahul Shome

  42 shared

• Chaitanya Mitash

  Amazon (United States)

  34 shared

• Bowen Wen

  28 shared

• Jingjin Yu

  23 shared

• Lydia E. Kavraki

  23 shared

• Athanasios Krontiris

  23 shared

• Andrew Kimmel

  Rutgers, The State University of New Jersey

  22 shared

Labs

• PRACSYS LabPI

  Research in robot learning, perception, and planning

Education

• Ph.D., Computer Science

  Rutgers, The State University of New Jersey

• M.S., Computer Science

  Rutgers, The State University of New Jersey

• B.S., Computer Science

  Rutgers, The State University of New Jersey

Awards & honors

• NSF NRI grant
• NASA early career grant
• NSF grant
• two new grants
• NSF SA&S grant

Similar researchers at Rutgers University

• Dimitris Metaxash-69
• Ahmed Elgammalh-50
• Tomasz Imielinskih-46