911 citations · 2.9k across the 99 of their papers we have counts for
124 papers
Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments
Michael G. Adam, Sebastian Eger, Martin Piccolrovazzi +10
As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the appli…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Lan-grasp: Using Large Language Models for Semantic Object Grasping and Placement
Reihaneh Mirjalili, Michael Krawez, Yannik Blei +3
In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping and placing. We leverage foundation models to equip the robot with a semantic under…
Few-Shot Panoptic Segmentation With Foundation Models
Markus Käppeler, Kürsat Petek, Niclas Vödisch +2
Current state-of-the-art methods for panoptic segmentation require an immense amount of annotated training data that is both arduous and expensive to obtain posing a significant ch…
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…
A Smart Robotic System for Industrial Plant Supervision
D. Adriana Gómez-Rosal, Max Bergau, Georg K. J. Fischer +10
In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous opera…
POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments
Jingxing Qian, Veronica Chatrath, James Servos +4
Simultaneous localization and mapping (SLAM) in slowly varying scenes is important for long-term robot task completion. Failing to detect scene changes may lead to inaccurate maps…
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…
Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry
Shengchao Yan, Baohe Zhang, Yuan Zhang +2
Geometric regularity, which leverages data symmetry, has been successfully incorporated into deep learning architectures such as CNNs, RNNs, GNNs, and Transformers. While this conc…
AutoGraph: Predicting Lane Graphs from Traffic Observations
Jannik Zürn, Ingmar Posner, Wolfram Burgard
Lane graph estimation is a long-standing problem in the context of autonomous driving. Previous works aimed at solving this problem by relying on large-scale, hand-annotated lane g…
End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization
Guangming Wang, Yu Zheng, Yuxuan Wu +5
Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation. A popular approach to robot localization is based on…
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…
FM-Loc: Using Foundation Models for Improved Vision-based Localization
Reihaneh Mirjalili, Michael Krawez, Wolfram Burgard
Visual place recognition is essential for vision-based robot localization and SLAM. Despite the tremendous progress made in recent years, place recognition in changing environments…
Audio Visual Language Maps for Robot Navigation
Chenguang Huang, Oier Mees, Andy Zeng +1
While interacting in the world is a multi-sensory experience, many robots continue to predominantly rely on visual perception to map and navigate in their environments. In this wor…
Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces
Johan Vertens, Nicolai Dorka, Tim Welschehold +2
The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still 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…
Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Nicolai Dorka, Tim Welschehold, Wolfram Burgard
Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However…
Learning and Aggregating Lane Graphs for Urban Automated Driving
Martin Büchner, Jannik Zürn, Ion-George Todoran +2
Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with co…
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…
EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Monish R. Nallapareddy, Kshitij Sirohi, Paulo L. J. Drews-Jr +3
Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decisi…
SkyEye: Self-Supervised Bird's-Eye-View Semantic Mapping Using Monocular Frontal View Images
Nikhil Gosala, Kürsat Petek, Paulo L. J. Drews-Jr +2
Bird's-Eye-View (BEV) semantic maps have become an essential component of automated driving pipelines due to the rich representation they provide for decision-making tasks. However…
Uncertainty-aware LiDAR Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current lear…
Visual Language Maps for Robot Navigation
Chenguang Huang, Oier Mees, Andy Zeng +1
Grounding language to the visual observations of a navigating agent can be performed using off-the-shelf visual-language models pretrained on Internet-scale data (e.g., image capti…
Grounding Language with Visual Affordances over Unstructured Data
Oier Mees, Jessica Borja-Diaz, Wolfram Burgard
Recent works have shown that Large Language Models (LLMs) can be applied to ground natural language to a wide variety of robot skills. However, in practice, learning multi-task, la…