Risto Kojcev
Papers
13
Total Citations
321
H-Index
8
About
Risto Kojcev is a multidisciplinary robotics researcher whose work spans medical robotics, continuum robot design, modular robot architectures, and reinforcement learning. He has made significant contributions to the field of ultrasound-guided robotic interventions, most notably demonstrating the reproducibility of robotic ultrasound acquisitions compared to expert operators (73 citations) and pioneering dual-robot systems for ultrasound-guided needle placement that seamlessly integrate planning, imaging, and action (64 citations). His innovative work on interlaced continuum robots — designed to safely navigate to hard-to-reach anatomical targets — has further solidified his reputation in surgical robotics (58 citations). Beyond medical applications, Kojcev has shaped the future of interoperable robotic systems through the Hardware Robot Operating System (H-ROS) and the Hardware Robot Information Model (HRIM), advocating for vendor-agnostic, modular robot components. More recently, he has advanced reinforcement learning for robotics, developing frameworks such as gym-gazebo2 and ROS2Learn that bridge simulation and real-world deployment. With over 300 cumulative citations, Kojcev's research uniquely connects clinical precision, hardware standardization, and intelligent autonomy, making him a notable voice in the next generation of robotics innovation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo25 citations · 2019
- 7Dissecting Robotics - historical overview and future perspectives16 citations · 2017
- 8ROS2Learn: a reinforcement learning framework for ROS 28 citations · 2019
- 9
- 10Evaluation of Deep Reinforcement Learning Methods for Modular Robots4 citations · 2018