564 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of FreiburgDE10 papers
- Georg Simon Ohm University of Applied Sciences NurembergDE5 papers
- Institut Élie Cartan de LorraineFR3 papers
- Lutheran University of Applied Sciences NurembergDE3 papers
- Université de LorraineFR3 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE2 papers
- Technical University of MunichDE2 papers
- University of KonstanzDE2 papers
- Bavarian Center for Applied Energy ResearchDE1 paper
- Bielefeld UniversityDE1 paper
- Centre for Tactile Internet with Human-in-the-LoopDE1 paper
- Columbia UniversityUS1 paper
14 papers · 1 filter
The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems
Paolo Leopardi, Heiko Hamann, Jonas Kuckling +1
Task specialization can improve the efficiency of multi-robot systems (MRSs). Previous works have investigated the emergence of task-specialist robot controllers through evolutiona…
On the Cost of Evolving Task Specialization in Multi-Robot Systems
Paolo Leopardi, Heiko Hamann, Jonas Kuckling +1
Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutio…
A roadmap for AI in robotics
Aude Billard, Alin Albu-Schaeffer, Michael Beetz +8
AI technologies, including deep learning, large-language models have gone from one breakthrough to the other. As a result, we are witnessing growing excitement in robotics at the p…
UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction
Nisarga Nilavadi, Andrey Rudenko, Timm Linder
We introduce a unified approach to forecast the dynamics of human keypoints along with the motion trajectory based on a short sequence of input poses. While many studies address ei…
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation
Niclas Vödisch, Kürsat Petek, Markus Käppeler +2
A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achie…
Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences
Kürsat Petek, Niclas Vödisch, Johannes Meyer +3
Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highl…
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Jonas Schramm, Niclas Vödisch, Kürsat Petek +4
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots. Although recent vision-onl…
Collaborative Dynamic 3D Scene Graphs for Automated Driving
Elias Greve, Martin Büchner, Niclas Vödisch +2
Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building a…
Fast yet predictable braking manoeuvers for real-time robot control
Mazin Hamad, Jesus Gutierrez-Moreno, Hugo T. M. Kussaba +5
This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain fe…
The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation
Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold +3
In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a…
CoVIO: Online Continual Learning for Visual-Inertial Odometry
Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard +1
Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains…
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation
Niclas Vödisch, Kürsat Petek, Wolfram Burgard +1
Operating a robot in the open world requires a high level of robustness with respect to previously unseen environments. Optimally, the robot is able to adapt by itself to new condi…
PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention
José Arce, Niclas Vödisch, Daniele Cattaneo +2
A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based met…
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard +1
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous L…