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Nico Hochgeschwender

Professor

University of Bremen

Germany

Research Interests

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Professor Nico Hochgeschwender is a faculty member at the University of Bremen in Germany. His research primarily focuses on the semantic composition of robotic solver algorithms, software engineering for autonomous robots, and machine learning practices in Earth Observation. He has published recent articles on topics including composable models for robotic mobile-manipulation tasks and simulation-based testing for mobile robots.

Articles (12)

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2026

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2025

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2025

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2021

Year:

2021

Collaborators (10)

Alcino Cunha

University of Minho

PORTUGAL

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Marija Slavkovik

Professor

University of Bergen

NORWAY

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Kevin Baum

Senior Researcher

Deutsches Forschungszentrum für Künstliche Intelligenz GmbH

GERMANY

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Thomas M Powers

Associate Professor

University of Delaware

UNITED STATES

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Anna Dobrosovestnova

TU Wien

AUSTRIA

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Helen Beebee

Professor of Philosophy of Science

University of Leeds

UNITED KINGDOM

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AJung Moon

McGill University

CANADA

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Raja Chatila

Professor

Sorbonne Université

FRANCE

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Masoumeh Mansouri

University of Birmingham

UNITED KINGDOM

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S. Kate Conroy

Adjunct Professor

Queensland University of Technology

AUSTRALIA

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Nico Hochgeschwender

Research Interests

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Articles (12)

Responsible Humanoids: A Contradiction in Terms?

Semantic composition of robotic solver algorithms on graph structures

From Composable Models to Correct-by-Construction Software for Contact-Rich Robotic Mobile-Manipulation Tasks

A Road Map for Responsible Robotics: Promoting Human Agency and Collaborative Efforts

Automated Behaviour-Driven Acceptance Testing of Robotic Systems

From Developer Needs to Domain-Specific Languages: Specifying Sceneries for Simulation-Based Validation of Mobile Robots

Planning Robot Placement for Object Grasping

Specification and Execution of Robotic Acceptance Tests for Object Sorting

Future Directions in Software Engineering for Autonomous Robots: An Agenda for Trustworthiness [Opinion]

Composable and executable scenarios for simulation-based testing of mobile robots

A qualitative study of Machine Learning practices and engineering challenges in Earth Observation

A qualitative study of Machine Learning practices and engineering challenges in Earth Observation

Collaborators (10)

Alcino Cunha

Marija Slavkovik

Kevin Baum

Thomas M Powers

Anna Dobrosovestnova

Helen Beebee

AJung Moon

Raja Chatila

Masoumeh Mansouri

S. Kate Conroy

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