68 citations · 179 across the 19 of their papers we have counts for
19 papers
STEM: Semantic Target Search and Exploration using MAVs in Cluttered Environments
Nikhil Sethi, Max Lodel, Laura Ferranti +2
Autonomous target search is crucial for deploying Micro Aerial Vehicles (MAVs) in emergency response and rescue missions. Existing approaches either focus on 2D semantic navigation…
Learning Transferable Motor Skills for Geometry-Aware Robotic Surface Tasks
Miroslav David, Karla Stepanova, Robert Babuska
Robotic surface-interaction tasks, such as spray painting or welding, require both accurate geometric planning and precise motion execution. While modern motion planners generate v…
Learning Semantic Priorities for Autonomous Target Search
Max Lodel, Nils Wilde, Robert Babuška +1
The use of semantic features can improve the efficiency of target search in unknown environments for robotic search and rescue missions. Current target search methods rely on train…
From Data to Safe Mobile Robot Navigation: An Efficient and Modular Robust MPC Design Pipeline
Dennis Benders, Johannes Köhler, Robert Babuška +2
Model predictive control (MPC) is a powerful strategy for planning and control in autonomous mobile robot navigation. However, ensuring safety in real-world deployments remains cha…
Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression
Jiří Kubalík, Robert Babuška
Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic progra…
From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback
Zhaoting Li, Rodrigo Pérez-Dattari, Robert Babuska +2
Behavior cloning (BC) optimizes policies by treating human demonstrations as pointwise action labels. While effective with accurate action labels, this formulation is brittle in pr…
ILeSiA: Interactive Learning of Robot Situational Awareness from Camera Input
Petr Vanc, Giovanni Franzese, Jan Kristof Behrens +4
Learning from demonstration is a promising approach for teaching robots new skills. However, a central challenge in the execution of acquired skills is the ability to recognize fau…
EAGERx: Graph-Based Framework for Sim2real Robot Learning
Bas van der Heijden, Jelle Luijkx, Laura Ferranti +2
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle com…
Embedded Hierarchical MPC for Autonomous Navigation
Dennis Benders, Johannes Köhler, Thijs Niesten +3
To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods pro…
A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
Erik Derner, Kristina Batistič, Jan Zahálka +1
As large language models (LLMs) permeate more and more applications, an assessment of their associated security risks becomes increasingly necessary. The potential for exploitation…
Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning
Luuk van den Bent, Tomás Coleman, Robert Babuška
Currently, truss tomato weighing and packaging require significant manual work. The main obstacle to automation lies in the difficulty of developing a reliable robotic grasping sys…
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression
Jiří Kubalík, Erik Derner, Robert Babuška
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a meth…
Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators
Jiri Sedlar, Karla Stepanova, Radoslav Skoviera +5
This paper 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…
SymFormer: End-to-end symbolic regression using transformer-based architecture
Martin Vastl, Jonáš Kulhánek, Jiří Kubalík +2
Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression.…
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
N. Passalis, S. Pedrazzi, R. Babuska +15
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…
Geometry-Based Grasping of Vine Tomatoes
Taeke de Haan, Padmaja Kulkarni, Robert Babuska
We propose a geometry-based grasping method for vine tomatoes. It relies on a computer-vision pipeline to identify the required geometric features of the tomatoes and of the truss…
GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection
Osama Mazhar, Robert Babuska, Jens Kober
Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are…
Vision-based Navigation Using Deep Reinforcement Learning
Jonáš Kulhánek, Erik Derner, Tim de Bruin +1
Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic envir…
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression
Erik Derner, Jiří Kubalík, Nicola Ancona +1
Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decis…