Selected development projects


Toolkits Data science The main purpose of pyCreeper is to wrap tens of lines of python code, required to produce graphs...


Multi-agent systems Java Games AI A-Life Creeper is a Java MVC framework for those who want to create multi-agent simulations (or games) and...

The Hive Mind

Flash AI The Hive Mind is a sci-fi puzzle game about insect-inspired construction with robots. The player...

Stardust Colonies

Games C++ AI Stardust Colonies is a strategy game in which player-controlled units are aware of and affected by...

NXC UserInteraction Library

Robots Lego Mindstorms The User Interaction (UIn) Library helps you create standard user interfaces (multi-line aligned...


The main purpose of pyCreeper is to wrap tens of lines of python code, required to produce graphs that look good for a publication, into functions. It takes away your need to understand various quirks of matplotlib and gives you back ready-to-use and well-documented code.

Citations count fails to measure the impact of research

Academic career progress is often judged by so-called "h-index", that measures how much your research is being cited. I recently had a detailed look at what exactly the citations, specifically those reported by Google Scholar, amount to. Only a relatively small fraction of the reported citations corresponded to research being applied or reproduced in a meaningful way.

V-REP, Gazebo or ARGoS? A robot simulators comparison

Let’s have a look at three commonly used open-source simulators for robotics: V-REP, Gazebo and ARGoS, to find out which one suits your project the best.

Designing Effective Roadmaps for Robotics Innovation

Automated factories, autonomous delivery drones, self-driving cars: these and similar technologies will soon touch every aspect of our lives. An engaging discussion about how these technologies are regulated and innovated took place at the IROS 2017 conference.

The Information-Cost-Reward framework for understanding robot swarm foraging

The Information-Cost-Reward (ICR) framework relates the way in which robots obtain and share information about where work needs to be done to the swarm’s ability to exploit that information in order to perform work efficiently in the context of a particular task and environment.