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Robert Babuska

10 accepted papers

2026

ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input

Petr Vanc, Giovanni Franzese, Jan Kristof Behrens, Cosimo Della Santina, Karla Stepanova, Jens Kober +1
ICRA 2026poster

Learning from demonstration is a promising way to teach robots new skills. However, a central challenge in executing acquired skills is the ability to recognize faults and prevent failures. This is essential since the demonstrations usually cover only a limited number of mostly successful cases. Dur…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators

Jirí Sedlár, Karla Stépánová, Radoslav Skoviera, Jan Kristof Behrens, Matús Tuna, Gabriela Sejnova +2
RA-L 2023

This letter introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera. Despite the significant progress of 6D pose estimation methods, their performance is usually limited for heavily occluded objects,

Cited by 7Source ↗code ↗Scholar ↗
2021

Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

Jonas Kulhanek, Erik Derner, Robert Babuska
RA-L 2021

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and

Cited by 58Source ↗Scholar ↗
2020

Object-Based Pose Graph for Dynamic Indoor Environments

Clara Gómez, Alejandra C. Hernández, Erik Derner, Ramón Barber, Robert Babuska
RA-L 2020

Relying on static representations of the environment limits the use of mapping methods in most real-world tasks. Real-world environments are dynamic and undergo changes that need to be handled through map adaptation. In this work, an object-based pose graph is proposed to solve the problem of mappin

Cited by 11Source ↗Scholar ↗
2020

Simultaneous task allocation and motion scheduling for complex tasks executed by multiple robots

Jan Kristof Behrens, Karla Stepanova, Robert Babuska
ICRA 2020poster

The coordination of multiple robots operating simultaneously in the same workspace requires the integration of task allocation and motion scheduling. We focus on tasks in which the robot's actions are not confined to small volumes, but can also occupy a large time-varying portion of the workspace, s…

Cited by 23Source ↗Scholar ↗
2018

Integrating State Representation Learning Into Deep Reinforcement Learning

Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska
RA-L 2018

Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies. This problem can be largely attributed to the fact that a state representation needs to be learned as a part of learning control policies, whi

Cited by 119Source ↗Scholar ↗
2018

Model-Plant Mismatch Compensation Using Reinforcement Learning

Ivan Koryakovskiy, Manuel Kudruss, Heike Vallery, Robert Babuska, Wouter Caarls
RA-L 2018

Learning-based approaches are suitable for the control of systems with unknown dynamics. However, learning from scratch involves many trials with exploratory actions until a good control policy is discovered. Real robots usually cannot withstand the exploratory actions and suffer damage. This proble

Cited by 41Source ↗Scholar ↗