Martin Ruskowski — accepted AI-conference papers

2026

Mapping the Efficiency Landscape of Small Language Models

Fabian Reichwald, Lukas Schiesser, Christiane Plociennik, Leonhard Kunz, Simon Pukrop, Martin Ruskowski +1

IJCAI 2026

Large language models (LLMs) dominate both everyday and specialized applications, but their high computational demand, energy consumption, and privacy risks are increasingly critiqued. Small language models (SLMs) mitigate these drawbacks and are gaining momentum in scenarios where full LLM capabili

Cited by 0 Scholar ↗

2021

Hybrid Data-Driven Modelling for Inverse Control of Hydraulic Excavators

Jonas Weigand, Julian Raible, Nico Zantopp, Ozan Demir, Adrian Trachte, Achim Wagner +1

IROS 2021poster

We present a comprehensive comparison of hybrid Data-Driven Control (DDC) applied on a hydraulic excavator. DDC offers a state-of-the-art, high performance control based on data and expert knowledge. On the one hand, expert knowledge is complex to adapt to each unique excavator requiring substantial…

Cited by 10 Source ↗Scholar ↗

2021

Optimal scheduling and non-cooperative distributed model predictive control for multiple robotic manipulators

Nigora Gafur, Vassilios Yfantis, Martin Ruskowski

IROS 2021poster

Application of multiple robotic manipulators in a shared workspace is still restricted to repetitive tasks limiting their flexible deployment for production systems. Still, existing motion control algorithms cannot be performed online for arbitrary environments in case of multiple manipulators coope…

Cited by 7 Source ↗Scholar ↗