Research

IROS26

Layout-independent actuation allocator for marine robots Yuya Hamamatsu, Maarja Kruusmaa, Asko Ristolainen Tallinn University of Technology Code (Coming soon) arXiv Abstract In this study, we propose a layout-independent control allocator capable of zero-shot deployment across diverse actuator configurations. The proposed method utilizes a learning pipeline that integrates a Graph Neural Network (GNN) and a Transformer to represent the robot’s geometric layout as a graph, along with a Mixture Density Network (MDN) to predict multi-modal control command distributions.

JOE2026

Strouhal-Aware Model Predictive Control for Efficient Multi-Fin Flapping Locomotion Yuya Hamamatsu, Zixi Chen, Maarja Kruusmaa, Asko Ristolainen Tallinn University of Technology Vrije Universiteit Brussel Code (Coming soon) arXiv Abstract Efficient flapping propulsion hinges on operating within a narrow Strouhal number window, a principle nature has converged upon for maximum thrust-to-power ratio. We translate this bioinspired empirical rule into real-time control, demonstrating it on an autonomous underwater vehicle driven by four soft fins.

ICRA25

Cross-platform Learning-based Fault Tolerant Surfacing Controller for Underwater Robots Yuya Hamamatsu, Walid Remmas, Jaan Rebane, Maarja Kruusmaa, Asko Ristolainen Tallinn University of Technology Code arXiv Abstract In this paper, we propose a novel cross-platform fault-tolerant surfacing controller for underwater robots, based on reinforcement learning (RL). Unlike conventional approaches, which require explicit identification of malfunctioning actuators, our method allows the robot to surface using only the remaining operational actuators without needing to pinpoint the failures.

RoboSoft25

Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate Model Yuya Hamamatsu*, Pavlo Kupyn* ** , Roza Gkliva*, Asko Ristolainen*, Maarja Kruusmaa* *Tallinn University of Technology, **Vilnius University Code arXiv Abstract This study presents a novel framework for precise force control of fin-actuated underwater robots by integrating a deep neural network (DNN)-based surrogate model with reinforcement learning (RL). To address the complex interactions with the underwater environment and the high experimental costs, a DNN surrogate model acts as a simulator for enabling efficient training for the RL agent.