Author name cluster

Stefan Sosnowski

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

The SeaClear system: An intelligent multi-robot solution for autonomous cleanup of marine debris on the seabed

Athina Ilioudi
Stefan Sosnowski
Elisabeth Banken
Petar Bevanda
Jan Brüdigam
Lucian Buşoniu
Yves Chardard
Cosmin Delea

Marine debris poses an alarming threat to ocean environments. Conventional methods of sea and ocean cleaning rely heavily on manual collection, a process that has repeatedly demonstrated its inefficiency and extensive demand for resources. This paper presents the SeaClear system, a novel multi-robot platform designed to autonomously detect and collect marine debris, thereby offering a more efficient solution to this environmental challenge. An overview of the system is presented, followed by a detailed description of each robot’s capabilities. Leveraging artificial intelligence, the system employs the deep-learning-based computer vision algorithm You Only Look Once (YOLO) for the detection of underwater litter, addressing the challenges of poor visibility and hydrodynamic disturbances of underwater environments. Additionally, the paper explores the implemented navigation and control methodologies, which are an essential part of the workflow of the system. The performance of the designed system is validated via field tests conducted in a real-world underwater environment. Finally, directions for future work are proposed.

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NeurIPS Conference 2023 Conference Paper

Koopman Kernel Regression

Petar Bevanda
Max Beier
Armin Lederer
Stefan Sosnowski
Eyke Hüllermeier
Sandra Hirche

Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e. g. , the state of an agent or the reward of a policy. Forecasts of such complex phenomena are commonly described by highly nonlinear dynamical systems, making their use in optimization-based decision-making challenging. Koopman operator theory offers a beneficial paradigm for addressing this problem by characterizing forecasts via linear time-invariant (LTI) ODEs, turning multi-step forecasts into sparse matrix multiplication. Though there exists a variety of learning approaches, they usually lack crucial learning-theoretic guarantees, making the behavior of the obtained models with increasing data and dimensionality unclear. We address the aforementioned by deriving a universal Koopman-invariant reproducing kernel Hilbert space (RKHS) that solely spans transformations into LTI dynamical systems. The resulting Koopman Kernel Regression (KKR) framework enables the use of statistical learning tools from function approximation for novel convergence results and generalization error bounds under weaker assumptions than existing work. Our experiments demonstrate superior forecasting performance compared to Koopman operator and sequential data predictors in RKHS.

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IROS Conference 2021 Conference Paper

Distributed Event- and Self-Triggered Coverage Control with Speed Constrained Unicycle Robots

Yuni Zhou
Lingxuan Kong
Stefan Sosnowski
Qingchen Liu
Sandra Hirche

Voronoi coverage control is a particular problem of importance in the area of multi-robot systems, which considers a network of multiple autonomous robots, tasked with optimally covering a large area. This is a common task for fleets of fixed-wing Unmanned Aerial Vehicles (UAVs), which are described in this work by a unicycle model with constant forward-speed constraints. We develop event-based control/communication algorithms to relax the resource requirements on wireless communication and control actuators, an important feature for battery-driven or otherwise energy-constrained systems. To overcome the drawback that the event-triggered algorithm requires continuous measurement of system states, we propose a self-triggered algorithm to estimate the next triggering time. Hardware experiments illustrate the theoretical results.

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IROS Conference 2012 Conference Paper

An emotional adaption approach to increase helpfulness towards a robot

Barbara Gonsior
Stefan Sosnowski
Malte Buss
Dirk Wollherr
Kolja Kühnlenz

This paper describes a new methodological approach and robot system to trigger more prosocial human reactions towards a robot by transferring social-psychological principles from human-human interaction to human-robot interaction (HRI). The main idea is to trigger increased helpfulness by proactively creating similarity through dynamic emotional adaption of the robot to the mood of the human. This is achieved in an explicit and implicit way: Explicitly, by a similarity-statement of the robot of being in the same mood as the user, and implicitly by controlling the affective parameters of facial and verbal expressions of a robot head in an interaction scenario such that the current values of the human mood in the dimensions of pleasure, arousal, and dominance (PAD) are matched. In a first step, this is accomplished by an initial self-assessment by the human participant to be extended by automatic emotion recognition modules in a later stage. The effectiveness of the approach is confirmed by significant experimental results.

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ICRA Conference 2009 Conference Paper

Navigation through urban environments by visual perception and interaction

Quirin Mühlbauer
Stefan Sosnowski
Tingting Xu
Tianguang Zhang
Kolja Kühnlenz
Martin Buss

In the autonomous city explorer (ACE) project a mobile robot is developed, which is capable of finding its way to a given destination in an unknown urban environment. An exemplary mission is to find the way from our institute to the Marienplatz, a public place in the center of Munich, without any prior knowledge or GPS information. Inspired by the behavior of humans in unknown environments, ACE must find its way by asking pedestrians. The route is about 1. 5 kilometers far and includes heavily traveled roads and crowded public places. In order to navigate safely in an unknown urban environment, some challenges arise for the vision system. Robust human detection, tracking and the estimation of human body poses is essential for natural interaction with pedestrians. Furthermore, the robot needs to be able to detect sidewalk and crossroads. A visual odometry system is used to support the conventional navigation. Outdoor experiments were conducted twice successfully. After about 5 hours and interacting with 25 and 38 persons respectively, ACE arrived the Marienplatz. This paper describes both, an architecture of the vision system used for ACE and the algorithms used to deal with the described challenges.

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ICRA Conference 2009 Conference Paper

The Autonomous City Explorer project

Andrea Maria Bauer
Klaas Klasing
Tingting Xu
Stefan Sosnowski
Georgios Lidoris
Quirin Mühlbauer
Tianguang Zhang
Florian Rohrmüller

This video presents the Autonomous City Explorer (ACE) project. Its goal was to create a robot capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. The robot had to find its way solely by interacting with pedestrians and building a topological representation of its surroundings. This video outlines the necessary ingredients for successful low-level navigation on sidewalks, information retrieval from pedestrians as well as the construction of a semantic representation of an urban environment. A system architecture for outdoor localization, traversability assessment, path planning, behavior selection and topological abstraction in urban environments is presented.

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IROS Conference 2006 Conference Paper

Design and Evaluation of Emotion-Display EDDIE

Stefan Sosnowski
Ansgar Bittermann
Kolja Kühnlenz
Martin Buss

This paper focuses on the development of EDDIE, a flexible low-cost emotion-display with 23 degrees of freedom. Actuators are assigned to particular action units of the facial action coding system (FACS). Emotion states represented by the circumplex model of affect are mapped to individual action units. Thereby, continuous, dynamic, and realistic emotion state transitions are achieved. EDDIE is largely developed and manufactured in a rapid-prototyping process. Miniature off-the-shelf mechatronics components are used providing high functionality at low-cost. Evaluations conducted in a user-study show that emotions can be recognized very well. Further experiments show that additional features adapted from animals have significant but small influence on the display of the human emotion 'disgust'

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IROS Conference 2006 Conference Paper

EDDIE - An Emotion Display with Dynamic Intuitive Expressions

Stefan Sosnowski
Kolja Kühnlenz
Martin Buss

EDDIE, a novel mechatronical emotion-display designed for dynamic non-verbal human-robot interaction is presented. A special feature are dynamic and realistic emotional state transitions. Therefore, the emotional state-space based on the circumplex model of affect is directly mapped to joint space. The display is largely developed and manufactured in a rapid-prototyping process. Only miniature off-the-shelf mechatronic components are used providing high functionality at low cost.

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