Jakub M. Tomczak

About

Jakub M. Tomczak is a researcher working at the intersection of evolutionary robotics, machine learning, and optimization, with particular expertise in the co-evolution of robot morphologies and controllers. His most influential work explores how modular robots can learn directed locomotion across diverse body plans, addressing one of the field's central challenges: equipping newborn robots with functional brains despite the mismatch that arises when bodies and controllers evolve simultaneously. This "Triangle of Life" problem — and how learning periods can mitigate it — runs as a thread through much of his research, culminating in studies on Lamarckian inheritance as a means of enhancing evolutionary robot systems. Beyond robotics, Tomczak has contributed to optimization methodology, developing a Bayesian-Evolutionary hybrid algorithm that meaningfully improves time efficiency in generate-and-test search (65 citations), and to causal machine learning, with work on data augmentation strategies for improving model generalization across unseen domains. Collectively, his publications have accumulated over 170 citations, reflecting growing community interest in embodied AI and robust optimization. His work is particularly valuable for researchers navigating the complex interplay between evolutionary computation, adaptive behavior, and real-world robot deployment.

Research Focus

Key Achievements

Top Papers

1. 1
   Time efficiency in optimization with a bayesian-Evolutionary algorithm

   65 citations · 2021
2. 2
   Learning directed locomotion in modular robots with evolvable morphologies

   26 citations · 2021
3. 3
   Selecting Data Augmentation for Simulating Interventions

   22 citations · 2020
4. 4
   Learning locomotion skills in evolvable robots

   15 citations · 2021
5. 5
   Learning Directed Locomotion in Modular Robots with Evolvable Morphologies

   14 citations · 2020
6. 6
   The Effects of Learning in Morphologically Evolving Robot Systems

   13 citations · 2022
7. 7
   Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm

   11 citations · 2020
8. 8
   Enhancing robot evolution through Lamarckian principles

   7 citations · 2023
9. 9
   The Effects of Learning in Morphologically Evolving Robot Systems

   3 citations · 2021

Key Collaborators

Contact & Links